SlideShare a Scribd company logo
1 of 52
Image Registration Methods for Reconstructing a Gene Expression Atlas of Early Zebrafish Embryogenesis Department Of Electronic Engineering  Technical School Of Telecommunications Engineering Technical University Of Madrid Evangelia Balanou Master Thesis European Postgraduate Program On Biomedical Engineering University of Patras – National Technical University of Athens
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Outline Introduction Motivation Problem Goal Image Registration Components Design and Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results and Evaluation Comparison of Registration Methods Atlas Construction Conclusions and Future Work
Introduction Motivation Problem to be solved
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Motivation ,[object Object]
Study embryonic development of vertebrates:
Vertebrate developmental disorders
Human hereditary disease
 Vertebrate model: zebrafish
Rapidly developing transparent       embryos ,[object Object]
Short generation timeEarly development of a zebrafish embryo
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Problem Quantitative spatio-temporal data at cellular level about gene expression required 		Provided by  Fluorescence In Situ Hybridization techniques and  Laser Scanning Microscopy Second gene expression pattern x One gene expression pattern y z
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Problem Qualitative spatio-temporal data at cellular level about gene expression required 		Provided by  Fluorescence In Situ Hybridization techniques and  Laser Scanning Microscopy ,[object Object],Image processing methods to integrate different expression patterns (from different embryos) into a 3-D gene expression atlas
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Goal Design and implement an image processing framework able to register different datasets with different gene expression patterns to a common template at a given developmental stage “Registration is the process of determining a geometrical transformation that aligns points in one view of an object with corresponding points in another view of that object or another object.” Template One dataset Template + registered image
Image Registration Fundamental task in image processing Various techniques (data, application)
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Image Registration Intensity-based : 	Calculates the transformation using voxel values alone Input: 2 images – fixed, moving     	Output: geometrical transformation Optimization problem Decomposed into a set of basic elements (defining different methods) Registration Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Transformation Registration Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters Defines the type of parameters whose values align the two images (search space) Spatial mapping of points from the fixed image space to points in the moving image space (inverse mapping)
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Interpolation Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters Evaluate moving image intensities at the mapped, non-grid positions
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Similarity Measure Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters A measure of “how well” fixed and transformed moving match each other Provides a quantitative criterion to be optimized over the search space (similarity measure function, S(T) ) The desired optimum may be one of the local ones
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Similarity Measures Correlation Coefficient  Fixed Image intensity (I2) Moving Image intensity (I1) Intensities in two images linearly related  As written, function to be maximized ,[object Object],Fixed Image intensity (I2) Intensities in two images statistically related  As written, function to be maximized  Moving Image intensity (I1)
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Optimization Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Parameters Transformation Initial Parameters Most complex component Starting from an initial set of parameters, iteratively  searches the optimal solution of the similarity measure function over the parameter space defined by the transformation Stops when stopping criterion is met
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Optimization Algorithms ,[object Object],Derivative of similarity measure function (S)wrt to each transformation parameter Attracted by local extrema ,[object Object],Initialization Mutation Recombination Selection Stochastic, population-based Global optimization technique – slow in computation
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Cost Function
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Local optimization Start
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Local optimization End
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Global optimization Start
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Global optimization End Capture range of correct optimum (initial parameter range or initialization)
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Resampling Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters Once a stopping criterion is met or iteration number has reached, the last transformation parameters are used to produce the registered image
Design & Implementation Implemented framework’s concept Different steps it is composed of
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Concept Goal: Design and implement an image processing framework able to register different datasets with different gene expression patterns to a common template at a given developmental stage Partial views Template embryo Nuclei channel Reference gene channel (goosecoid) Another gene channel *All images are 3D and grayscale *Colourmap just for visualization
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Concept Partial view of another embryo Partial views Template embryo Nuclei channel Nuclei channel Registration Reference gene channel Reference gene channel Another gene channel *All images are 3D and grayscale *Reference gene (position): goosecoid (gsc) *Colourmap just for visualization Gene expression atlas
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Overview registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered  image Rotation centre Transformation  Parameters  Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Gravity centres Registration pipeline initialization Partial embryo view, third channel preprocessing transformation Third channel  mapped Atlas construction pipeline
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Registration Pipeline registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered  image Rotation centre Transformation  Parameters  Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Gravity centres Registration pipeline initialization Partial embryo view, third channel preprocessing transformation Third channel  mapped Atlas construction pipeline Purpose: Determine the transformation parameters that bring into spatial alignment the template and one partial view
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Preprocessing & Addition Step  registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered  image Rotation centre Transformation  Parameters  Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Registration pipeline Purpose: Remove noise, blur, downsample, threshold Combine information from nuclei and gsc channels into a single image
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Preprocessing & Addition Step  addition preprocessing Original nuclei channel Combined image Original gsc channel preprocessing ,[object Object]
Weighted Addition addition preprocessing preprocessing 0 255 Combined image Original channels   Preprocessed channels Resolution: 512 x 512 x 465 Voxel size: 1.517 x 1.517 x 1,509μm Resolution: 128 x 128 x 116 Voxel size: 6.068 x 6.068 x 6.036μm
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Initialization Step registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered  image Rotation centre Transformation  Parameters  Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Registration pipeline Purpose: Initial positioning of moving to fixed image’s space  	(no initial parameters in registration)  	If NOT sufficient overlapping, registration fails
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Initialization Step Preprocessed  partial embryo view, nuclei channel (binary) Preprocessed  partial embryo view, gsc channel initialization Moving image Initialized Moving image Rotation centre Fixedimage Preprocessed whole embryo view, nuclei channel (binary) Preprocessed whole embryo view, gsc channel Based on nature of data (nuclei and gsc channel) For both views one gravity centre from each channel  The resulting four points define a spatial transformation that is applied on the moving image
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Initialization Step nmoving gscmoving y gscmoving Rotation axis nfixed vF nmoving translation Moving (partial view) Rotation angle gscfixed Translated nmoving vM Translated nmoving nfixed x z gscfixed *Blue/Orange-nuclei   Green/Yellow-gsc expression pattern Fixed (template view)
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Initialization Step Before initialization Fixed Image Initialized Moving After Initialization Fixed (template) + Initialized Moving (partial) Partial view before and after initialization *Blue/Orange-nuclei   Green/Yellow-gsc expression pattern
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Registration Step registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered  image Rotation centre Transformation  Parameters  Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Registration pipeline Purpose: Find the transformation parameters that register the initialized moving image to the fixed
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Registration Step Implemented Registration step or Correlation Coefficient Fixed image TransformationParameters Mutual Information or Differential Evolution Resampling Initialized Movingimage Trilinear Interpolation Gradient Descent Registeredimage Global, Rigid 3D Transformation Initial Parameters (rotation centre) ,[object Object],-> Assumption:  embryos  similar in size and shape -> 3 rotations + 3 translation = 6 transformation parameters ,[object Object],[object Object]
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Atlas Construction Pipeline registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered  image Rotation centre Transformation  Parameters  Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Gravity centres Registration pipeline initialization Partial embryo view, third channel preprocessing transformation Third channel  mapped Atlas construction pipeline Purpose: Transformation of the third channel of the partial view 	Only transformation step is implemented as a new program
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Atlas Construction Pipeline Atlas Construction Pipeline       ->  Apply Transformation Parameters Partial view  White-nuclei Red-gsc expression pattern Green-snail expression pattern Template Orange-nuclei Yellow-gsc expression pattern Registration Pipeline       ->  Transformation Parameters
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Tools Development Insight Segmentation and Registration Toolkit Available at www.itk.org CMake Available at www.cmake.org Microsoft Visual Studio 2008 Visualization ,[object Object]
Available at http://amide.sourceforge.net/
Amira
Commercial product,[object Object]
Results & Evaluation
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Data Developmental stage: Shield (6 hpf) Framework tested with six datasets (six embryos) One template, one whole embryo view Partial views of five different embryos Animal Dorsal Ventral Vegetal Template embryo Partial view nuclei channel gsc channel co-stained gene expression pattern e.g. snail * Images provided by: DEPSN , France
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Questions Does the implemented framework succeed in registering our data? What is the combination of similarity measure and optimization algorithm that results in a successful registration? In other words… What is the most appropriate registration method for our application?
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Preprocessing & Addition Original channels   Preprocessed channels Combined image preprocessing addition Template preprocessing Slice Volume rendering Fixed image addition preprocessing One partial View preprocessing Slice Volume rendering One moving image Framework works with 2 datasets each time Preprocessing: smoothed, downsampled, nuclei channel turned to binary Addition: nuclei and gsc channels combined into a single image 5 partial -> 5 iterations (6 images in total – 1 fixed, 5 moving)
Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration      Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas  Conclusions & Future Work Initialization Template Image Template Image Template Image Initialized Partial 2 Initialized Partial 3 Initialized Partial 1 Template + Initialized partial 1 Template + Initialized partial 2 Template + Initialized partial 3 Template Image Template Image Initialized Partial4 Initialized Partial 5 Initialization looks  promising… Template + Initialized partial 5 Template + Initialized partial 4 *Blue/Orange-nuclei   Green/Yellow-gsc expression pattern

More Related Content

What's hot

Orthorectification and triangulation
Orthorectification and triangulationOrthorectification and triangulation
Orthorectification and triangulationMesfin Yeshitla
 
Anatomy of the Sellar Region (A.GH)
Anatomy of the Sellar Region (A.GH)Anatomy of the Sellar Region (A.GH)
Anatomy of the Sellar Region (A.GH)Ahmed Ghoul
 
Raster Vs. Vector Presentation1
Raster Vs. Vector Presentation1Raster Vs. Vector Presentation1
Raster Vs. Vector Presentation1cdoeberl
 
Diffusion Weighted MRI (2011-09-29 이정원)
Diffusion Weighted MRI (2011-09-29 이정원)Diffusion Weighted MRI (2011-09-29 이정원)
Diffusion Weighted MRI (2011-09-29 이정원)이정원 JeongwonLee
 
Computer Vision Structure from motion
Computer Vision Structure from motionComputer Vision Structure from motion
Computer Vision Structure from motionWael Badawy
 
Writing the report for doctoral confirmation at Massey University, New Zealand
Writing the report for doctoral confirmation at Massey University, New ZealandWriting the report for doctoral confirmation at Massey University, New Zealand
Writing the report for doctoral confirmation at Massey University, New ZealandMartin McMorrow
 
Nuclear medicine in musculoskeletal disorders
Nuclear medicine in musculoskeletal disordersNuclear medicine in musculoskeletal disorders
Nuclear medicine in musculoskeletal disordersfatmahoceny
 
Imaging in acute stroke
Imaging in acute stroke Imaging in acute stroke
Imaging in acute stroke AlwineAnto
 
3D visualisation of medical images
3D visualisation of medical images3D visualisation of medical images
3D visualisation of medical imagesShashank
 
Cardiac MRI basics
Cardiac MRI basicsCardiac MRI basics
Cardiac MRI basicsshaikAsma10
 
Digital image processing
Digital image processingDigital image processing
Digital image processingSangavi G
 
Stereo Matching by Deep Learning
Stereo Matching by Deep LearningStereo Matching by Deep Learning
Stereo Matching by Deep LearningYu Huang
 
MRI of Knee joint-- hossam massoud
MRI of Knee joint-- hossam massoudMRI of Knee joint-- hossam massoud
MRI of Knee joint-- hossam massoudHossam Massoud
 
Image enhancement
Image enhancementImage enhancement
Image enhancementjuhi mishra
 
How to read a cervical MRI
How to read a cervical MRIHow to read a cervical MRI
How to read a cervical MRISpiro Antoniades
 
Basic and advanced mri imaging sequences in brain
Basic and advanced mri imaging sequences in brainBasic and advanced mri imaging sequences in brain
Basic and advanced mri imaging sequences in brainDev Lakhera
 

What's hot (20)

Orthorectification and triangulation
Orthorectification and triangulationOrthorectification and triangulation
Orthorectification and triangulation
 
Anatomy of the Sellar Region (A.GH)
Anatomy of the Sellar Region (A.GH)Anatomy of the Sellar Region (A.GH)
Anatomy of the Sellar Region (A.GH)
 
Raster Vs. Vector Presentation1
Raster Vs. Vector Presentation1Raster Vs. Vector Presentation1
Raster Vs. Vector Presentation1
 
SAR-Guidebook (1).pptx
SAR-Guidebook (1).pptxSAR-Guidebook (1).pptx
SAR-Guidebook (1).pptx
 
4D Flow MRI
4D Flow MRI4D Flow MRI
4D Flow MRI
 
Diffusion Weighted MRI (2011-09-29 이정원)
Diffusion Weighted MRI (2011-09-29 이정원)Diffusion Weighted MRI (2011-09-29 이정원)
Diffusion Weighted MRI (2011-09-29 이정원)
 
Computer Vision Structure from motion
Computer Vision Structure from motionComputer Vision Structure from motion
Computer Vision Structure from motion
 
Writing the report for doctoral confirmation at Massey University, New Zealand
Writing the report for doctoral confirmation at Massey University, New ZealandWriting the report for doctoral confirmation at Massey University, New Zealand
Writing the report for doctoral confirmation at Massey University, New Zealand
 
Nuclear medicine in musculoskeletal disorders
Nuclear medicine in musculoskeletal disordersNuclear medicine in musculoskeletal disorders
Nuclear medicine in musculoskeletal disorders
 
Imaging in acute stroke
Imaging in acute stroke Imaging in acute stroke
Imaging in acute stroke
 
3D visualisation of medical images
3D visualisation of medical images3D visualisation of medical images
3D visualisation of medical images
 
Cardiac MRI basics
Cardiac MRI basicsCardiac MRI basics
Cardiac MRI basics
 
Digital image processing
Digital image processingDigital image processing
Digital image processing
 
Stereo Matching by Deep Learning
Stereo Matching by Deep LearningStereo Matching by Deep Learning
Stereo Matching by Deep Learning
 
MRI of Knee joint-- hossam massoud
MRI of Knee joint-- hossam massoudMRI of Knee joint-- hossam massoud
MRI of Knee joint-- hossam massoud
 
Image enhancement
Image enhancementImage enhancement
Image enhancement
 
How to read a cervical MRI
How to read a cervical MRIHow to read a cervical MRI
How to read a cervical MRI
 
Mri of knee
Mri of kneeMri of knee
Mri of knee
 
MRI Artefacts
MRI ArtefactsMRI Artefacts
MRI Artefacts
 
Basic and advanced mri imaging sequences in brain
Basic and advanced mri imaging sequences in brainBasic and advanced mri imaging sequences in brain
Basic and advanced mri imaging sequences in brain
 

Viewers also liked

Multi modality medical image registration
Multi modality medical image registrationMulti modality medical image registration
Multi modality medical image registrationHabib Baluwala
 
Registration Optimization
Registration OptimizationRegistration Optimization
Registration OptimizationShana Masterson
 
Fast Mesh-Based Medical Image Registration
Fast Mesh-Based Medical Image RegistrationFast Mesh-Based Medical Image Registration
Fast Mesh-Based Medical Image RegistrationAhmadreza Baghaie
 
Smaa : enhanced morphological anti-aliasing
Smaa : enhanced morphological anti-aliasingSmaa : enhanced morphological anti-aliasing
Smaa : enhanced morphological anti-aliasingJaeyun Lee
 
ITK Tutorial Presentation Slides-947
ITK Tutorial Presentation Slides-947ITK Tutorial Presentation Slides-947
ITK Tutorial Presentation Slides-947Kitware Kitware
 
Issues in Image Registration and Image similarity based on mutual information
Issues in Image Registration and Image similarity based on mutual informationIssues in Image Registration and Image similarity based on mutual information
Issues in Image Registration and Image similarity based on mutual informationDarshana Mistry
 
22 Machine Learning Feature Selection
22 Machine Learning Feature Selection22 Machine Learning Feature Selection
22 Machine Learning Feature SelectionAndres Mendez-Vazquez
 
Lecture 06 geometric transformations and image registration
Lecture 06 geometric transformations and image registrationLecture 06 geometric transformations and image registration
Lecture 06 geometric transformations and image registrationobertksg
 
Anti-Aliasing Methods in CryENGINE 3
Anti-Aliasing Methods in CryENGINE 3Anti-Aliasing Methods in CryENGINE 3
Anti-Aliasing Methods in CryENGINE 3Tiago Sousa
 
Medical imaging summary 1
Medical imaging summary 1Medical imaging summary 1
Medical imaging summary 1sirrainbow
 
Image registration and data fusion techniques.pptx latest save
Image registration and data fusion techniques.pptx latest saveImage registration and data fusion techniques.pptx latest save
Image registration and data fusion techniques.pptx latest saveM'dee Phechudi
 
Digital Image Processing: Image Segmentation
Digital Image Processing: Image SegmentationDigital Image Processing: Image Segmentation
Digital Image Processing: Image SegmentationMostafa G. M. Mostafa
 
Frequency Domain Image Enhancement Techniques
Frequency Domain Image Enhancement TechniquesFrequency Domain Image Enhancement Techniques
Frequency Domain Image Enhancement TechniquesDiwaker Pant
 
Image segmentation ppt
Image segmentation pptImage segmentation ppt
Image segmentation pptGichelle Amon
 
Feature selection concepts and methods
Feature selection concepts and methodsFeature selection concepts and methods
Feature selection concepts and methodsReza Ramezani
 

Viewers also liked (18)

Multi modality medical image registration
Multi modality medical image registrationMulti modality medical image registration
Multi modality medical image registration
 
Registration Optimization
Registration OptimizationRegistration Optimization
Registration Optimization
 
Fast Mesh-Based Medical Image Registration
Fast Mesh-Based Medical Image RegistrationFast Mesh-Based Medical Image Registration
Fast Mesh-Based Medical Image Registration
 
Smaa : enhanced morphological anti-aliasing
Smaa : enhanced morphological anti-aliasingSmaa : enhanced morphological anti-aliasing
Smaa : enhanced morphological anti-aliasing
 
Image Fusion
Image FusionImage Fusion
Image Fusion
 
ITK Tutorial Presentation Slides-947
ITK Tutorial Presentation Slides-947ITK Tutorial Presentation Slides-947
ITK Tutorial Presentation Slides-947
 
Issues in Image Registration and Image similarity based on mutual information
Issues in Image Registration and Image similarity based on mutual informationIssues in Image Registration and Image similarity based on mutual information
Issues in Image Registration and Image similarity based on mutual information
 
22 Machine Learning Feature Selection
22 Machine Learning Feature Selection22 Machine Learning Feature Selection
22 Machine Learning Feature Selection
 
Lecture 06 geometric transformations and image registration
Lecture 06 geometric transformations and image registrationLecture 06 geometric transformations and image registration
Lecture 06 geometric transformations and image registration
 
Anti-Aliasing Methods in CryENGINE 3
Anti-Aliasing Methods in CryENGINE 3Anti-Aliasing Methods in CryENGINE 3
Anti-Aliasing Methods in CryENGINE 3
 
Medical imaging summary 1
Medical imaging summary 1Medical imaging summary 1
Medical imaging summary 1
 
Image registration and data fusion techniques.pptx latest save
Image registration and data fusion techniques.pptx latest saveImage registration and data fusion techniques.pptx latest save
Image registration and data fusion techniques.pptx latest save
 
Digital Image Processing: Image Segmentation
Digital Image Processing: Image SegmentationDigital Image Processing: Image Segmentation
Digital Image Processing: Image Segmentation
 
Frequency Domain Image Enhancement Techniques
Frequency Domain Image Enhancement TechniquesFrequency Domain Image Enhancement Techniques
Frequency Domain Image Enhancement Techniques
 
Image segmentation ppt
Image segmentation pptImage segmentation ppt
Image segmentation ppt
 
Feature selection concepts and methods
Feature selection concepts and methodsFeature selection concepts and methods
Feature selection concepts and methods
 
Image processing ppt
Image processing pptImage processing ppt
Image processing ppt
 
A METHODICAL WAY OF IMAGE REGISTRATION IN DIGITAL IMAGE PROCESSING
A METHODICAL WAY OF IMAGE REGISTRATION IN DIGITAL IMAGE PROCESSING A METHODICAL WAY OF IMAGE REGISTRATION IN DIGITAL IMAGE PROCESSING
A METHODICAL WAY OF IMAGE REGISTRATION IN DIGITAL IMAGE PROCESSING
 

Similar to Image registration methods for zebrafish gene atlas

Proceedings of the 2015 Industrial and Systems Engineering Res.docx
Proceedings of the 2015 Industrial and Systems Engineering Res.docxProceedings of the 2015 Industrial and Systems Engineering Res.docx
Proceedings of the 2015 Industrial and Systems Engineering Res.docxwkyra78
 
Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172Editor IJARCET
 
Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172Editor IJARCET
 
1998 - Thesis JL Pacherie Parallel perators
1998 - Thesis JL Pacherie Parallel perators1998 - Thesis JL Pacherie Parallel perators
1998 - Thesis JL Pacherie Parallel peratorsJean-Lin Pacherie, Ph.D.
 
2cee Master Cocomo20071
2cee Master Cocomo200712cee Master Cocomo20071
2cee Master Cocomo20071CS, NcState
 
Brian muirhead v1-27-12
Brian muirhead v1-27-12Brian muirhead v1-27-12
Brian muirhead v1-27-12NASAPMC
 
Software Engineering Fundamentals
Software Engineering FundamentalsSoftware Engineering Fundamentals
Software Engineering FundamentalsRahul Sudame
 
Modeling Search Computing Applications
Modeling Search Computing ApplicationsModeling Search Computing Applications
Modeling Search Computing ApplicationsMarco Brambilla
 
IRJET- Analysis of Vehicle Number Plate Recognition
IRJET- Analysis of Vehicle Number Plate RecognitionIRJET- Analysis of Vehicle Number Plate Recognition
IRJET- Analysis of Vehicle Number Plate RecognitionIRJET Journal
 
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...csandit
 
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...cscpconf
 
An approach for software effort estimation using fuzzy numbers and genetic al...
An approach for software effort estimation using fuzzy numbers and genetic al...An approach for software effort estimation using fuzzy numbers and genetic al...
An approach for software effort estimation using fuzzy numbers and genetic al...csandit
 
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
 
SHAHBAZ_TECHNICAL_SEMINAR.docx
SHAHBAZ_TECHNICAL_SEMINAR.docxSHAHBAZ_TECHNICAL_SEMINAR.docx
SHAHBAZ_TECHNICAL_SEMINAR.docxShahbazKhan77289
 
Graphical Analysis
Graphical AnalysisGraphical Analysis
Graphical AnalysisCIToolkit
 

Similar to Image registration methods for zebrafish gene atlas (20)

Proceedings of the 2015 Industrial and Systems Engineering Res.docx
Proceedings of the 2015 Industrial and Systems Engineering Res.docxProceedings of the 2015 Industrial and Systems Engineering Res.docx
Proceedings of the 2015 Industrial and Systems Engineering Res.docx
 
Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172
 
Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172Volume 2-issue-6-2165-2172
Volume 2-issue-6-2165-2172
 
1998 - Thesis JL Pacherie Parallel perators
1998 - Thesis JL Pacherie Parallel perators1998 - Thesis JL Pacherie Parallel perators
1998 - Thesis JL Pacherie Parallel perators
 
WorkExamples
WorkExamplesWorkExamples
WorkExamples
 
Rnd
RndRnd
Rnd
 
2cee Master Cocomo20071
2cee Master Cocomo200712cee Master Cocomo20071
2cee Master Cocomo20071
 
Brian muirhead v1-27-12
Brian muirhead v1-27-12Brian muirhead v1-27-12
Brian muirhead v1-27-12
 
Data Mining and Analytics
Data Mining and AnalyticsData Mining and Analytics
Data Mining and Analytics
 
Software Engineering Fundamentals
Software Engineering FundamentalsSoftware Engineering Fundamentals
Software Engineering Fundamentals
 
Modeling Search Computing Applications
Modeling Search Computing ApplicationsModeling Search Computing Applications
Modeling Search Computing Applications
 
-linkedin
-linkedin-linkedin
-linkedin
 
IRJET- Analysis of Vehicle Number Plate Recognition
IRJET- Analysis of Vehicle Number Plate RecognitionIRJET- Analysis of Vehicle Number Plate Recognition
IRJET- Analysis of Vehicle Number Plate Recognition
 
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
 
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...
 
An approach for software effort estimation using fuzzy numbers and genetic al...
An approach for software effort estimation using fuzzy numbers and genetic al...An approach for software effort estimation using fuzzy numbers and genetic al...
An approach for software effort estimation using fuzzy numbers and genetic al...
 
Slides chapter 15
Slides chapter 15Slides chapter 15
Slides chapter 15
 
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...
 
SHAHBAZ_TECHNICAL_SEMINAR.docx
SHAHBAZ_TECHNICAL_SEMINAR.docxSHAHBAZ_TECHNICAL_SEMINAR.docx
SHAHBAZ_TECHNICAL_SEMINAR.docx
 
Graphical Analysis
Graphical AnalysisGraphical Analysis
Graphical Analysis
 

Recently uploaded

Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 

Recently uploaded (20)

Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 

Image registration methods for zebrafish gene atlas

  • 1. Image Registration Methods for Reconstructing a Gene Expression Atlas of Early Zebrafish Embryogenesis Department Of Electronic Engineering Technical School Of Telecommunications Engineering Technical University Of Madrid Evangelia Balanou Master Thesis European Postgraduate Program On Biomedical Engineering University of Patras – National Technical University of Athens
  • 2. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Outline Introduction Motivation Problem Goal Image Registration Components Design and Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results and Evaluation Comparison of Registration Methods Atlas Construction Conclusions and Future Work
  • 4.
  • 9.
  • 10. Short generation timeEarly development of a zebrafish embryo
  • 11. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Problem Quantitative spatio-temporal data at cellular level about gene expression required Provided by Fluorescence In Situ Hybridization techniques and Laser Scanning Microscopy Second gene expression pattern x One gene expression pattern y z
  • 12.
  • 13. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Goal Design and implement an image processing framework able to register different datasets with different gene expression patterns to a common template at a given developmental stage “Registration is the process of determining a geometrical transformation that aligns points in one view of an object with corresponding points in another view of that object or another object.” Template One dataset Template + registered image
  • 14. Image Registration Fundamental task in image processing Various techniques (data, application)
  • 15. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Image Registration Intensity-based : Calculates the transformation using voxel values alone Input: 2 images – fixed, moving Output: geometrical transformation Optimization problem Decomposed into a set of basic elements (defining different methods) Registration Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters
  • 16. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Transformation Registration Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters Defines the type of parameters whose values align the two images (search space) Spatial mapping of points from the fixed image space to points in the moving image space (inverse mapping)
  • 17. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Interpolation Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters Evaluate moving image intensities at the mapped, non-grid positions
  • 18. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Similarity Measure Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters A measure of “how well” fixed and transformed moving match each other Provides a quantitative criterion to be optimized over the search space (similarity measure function, S(T) ) The desired optimum may be one of the local ones
  • 19.
  • 20. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Optimization Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Parameters Transformation Initial Parameters Most complex component Starting from an initial set of parameters, iteratively searches the optimal solution of the similarity measure function over the parameter space defined by the transformation Stops when stopping criterion is met
  • 21.
  • 22. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Cost Function
  • 23. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Local optimization Start
  • 24. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Local optimization End
  • 25. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Global optimization Start
  • 26. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Global optimization End Capture range of correct optimum (initial parameter range or initialization)
  • 27. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Resampling Similarity measure Fixedimage TransformationParameters Movingimage Interpolation Optimization Resampling Registeredimage Transformation Initial Parameters Once a stopping criterion is met or iteration number has reached, the last transformation parameters are used to produce the registered image
  • 28. Design & Implementation Implemented framework’s concept Different steps it is composed of
  • 29. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Concept Goal: Design and implement an image processing framework able to register different datasets with different gene expression patterns to a common template at a given developmental stage Partial views Template embryo Nuclei channel Reference gene channel (goosecoid) Another gene channel *All images are 3D and grayscale *Colourmap just for visualization
  • 30. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Concept Partial view of another embryo Partial views Template embryo Nuclei channel Nuclei channel Registration Reference gene channel Reference gene channel Another gene channel *All images are 3D and grayscale *Reference gene (position): goosecoid (gsc) *Colourmap just for visualization Gene expression atlas
  • 31. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Overview registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered image Rotation centre Transformation Parameters Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Gravity centres Registration pipeline initialization Partial embryo view, third channel preprocessing transformation Third channel mapped Atlas construction pipeline
  • 32. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Registration Pipeline registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered image Rotation centre Transformation Parameters Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Gravity centres Registration pipeline initialization Partial embryo view, third channel preprocessing transformation Third channel mapped Atlas construction pipeline Purpose: Determine the transformation parameters that bring into spatial alignment the template and one partial view
  • 33. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Preprocessing & Addition Step registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered image Rotation centre Transformation Parameters Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Registration pipeline Purpose: Remove noise, blur, downsample, threshold Combine information from nuclei and gsc channels into a single image
  • 34.
  • 35. Weighted Addition addition preprocessing preprocessing 0 255 Combined image Original channels Preprocessed channels Resolution: 512 x 512 x 465 Voxel size: 1.517 x 1.517 x 1,509μm Resolution: 128 x 128 x 116 Voxel size: 6.068 x 6.068 x 6.036μm
  • 36. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Initialization Step registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered image Rotation centre Transformation Parameters Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Registration pipeline Purpose: Initial positioning of moving to fixed image’s space (no initial parameters in registration) If NOT sufficient overlapping, registration fails
  • 37. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Initialization Step Preprocessed partial embryo view, nuclei channel (binary) Preprocessed partial embryo view, gsc channel initialization Moving image Initialized Moving image Rotation centre Fixedimage Preprocessed whole embryo view, nuclei channel (binary) Preprocessed whole embryo view, gsc channel Based on nature of data (nuclei and gsc channel) For both views one gravity centre from each channel The resulting four points define a spatial transformation that is applied on the moving image
  • 38. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Initialization Step nmoving gscmoving y gscmoving Rotation axis nfixed vF nmoving translation Moving (partial view) Rotation angle gscfixed Translated nmoving vM Translated nmoving nfixed x z gscfixed *Blue/Orange-nuclei Green/Yellow-gsc expression pattern Fixed (template view)
  • 39. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Initialization Step Before initialization Fixed Image Initialized Moving After Initialization Fixed (template) + Initialized Moving (partial) Partial view before and after initialization *Blue/Orange-nuclei Green/Yellow-gsc expression pattern
  • 40. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Registration Step registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered image Rotation centre Transformation Parameters Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Registration pipeline Purpose: Find the transformation parameters that register the initialized moving image to the fixed
  • 41.
  • 42. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Atlas Construction Pipeline registration addition initialization Partial embryo view, nuclei channel Initialized Moving image preprocessing Moving image Partial embryo view, gsc channel preprocessing Registered image Rotation centre Transformation Parameters Whole embryo view, nuclei channel addition preprocessing Fixedimage Fixedimage Whole embryo view, gsc channel preprocessing Gravity centres Registration pipeline initialization Partial embryo view, third channel preprocessing transformation Third channel mapped Atlas construction pipeline Purpose: Transformation of the third channel of the partial view Only transformation step is implemented as a new program
  • 43. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Atlas Construction Pipeline Atlas Construction Pipeline -> Apply Transformation Parameters Partial view White-nuclei Red-gsc expression pattern Green-snail expression pattern Template Orange-nuclei Yellow-gsc expression pattern Registration Pipeline -> Transformation Parameters
  • 44.
  • 46. Amira
  • 47.
  • 49. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Data Developmental stage: Shield (6 hpf) Framework tested with six datasets (six embryos) One template, one whole embryo view Partial views of five different embryos Animal Dorsal Ventral Vegetal Template embryo Partial view nuclei channel gsc channel co-stained gene expression pattern e.g. snail * Images provided by: DEPSN , France
  • 50. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Questions Does the implemented framework succeed in registering our data? What is the combination of similarity measure and optimization algorithm that results in a successful registration? In other words… What is the most appropriate registration method for our application?
  • 51. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Preprocessing & Addition Original channels Preprocessed channels Combined image preprocessing addition Template preprocessing Slice Volume rendering Fixed image addition preprocessing One partial View preprocessing Slice Volume rendering One moving image Framework works with 2 datasets each time Preprocessing: smoothed, downsampled, nuclei channel turned to binary Addition: nuclei and gsc channels combined into a single image 5 partial -> 5 iterations (6 images in total – 1 fixed, 5 moving)
  • 52. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Initialization Template Image Template Image Template Image Initialized Partial 2 Initialized Partial 3 Initialized Partial 1 Template + Initialized partial 1 Template + Initialized partial 2 Template + Initialized partial 3 Template Image Template Image Initialized Partial4 Initialized Partial 5 Initialization looks promising… Template + Initialized partial 5 Template + Initialized partial 4 *Blue/Orange-nuclei Green/Yellow-gsc expression pattern
  • 53. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Method Evaluation Four different methods implemented Evaluation only by visual inspection of the results Optimization algorithms not comparable unless running with optimized parameters Lack of golden standard Point-to-point correspondence does not exist (different embryos)
  • 54.
  • 55. Global optimization algorithm is still computing (D.E. not suitable)
  • 56.
  • 58. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Summary - Conclusions Goal achieved Designed and implemented an image processing framework able to map different gene expression patterns on a common template (for a given developmental stage) Key points Addition: Combine information from two channels Initialization: Solves the problem of capture range for optimization Registration Method: Correlation Coefficient + Gradient Descent
  • 59. Outline Introduction Motivation Problem Goal ImageRegistration Transformation Interpolation Similarity Measure Optimization Resampling Design & Implementation Concept Overview Registration Pipeline Atlas Construction Pipeline Tools Implementation Results & Evaluation Comparison of Registration Methods Atlas Conclusions & Future Work Conclusions-Future work Advantages Modularity Configurability Semi-automated Future work More datasets -> more gene expression patterns Other developmental stages Validated with known gene regulatory networks
  • 60. Thanks to… Biomedical Image Technologies Laboratory (BIT) Technical School Of Telecommunications Engineering (ETSIT) Technical University of Madrid (UPM)
  • 61. Thank you for your attention

Editor's Notes

  1. Present yourself
  2. Movie (24 hpf):Zygote, cleavage (volume constant), blastula (MBT, epiboly), gastrula (germ layers formed, involves complex cell movements, shield D-V axis), segmentation (first cells differentiate morphologically)
  3. Tools for studying the spatial characteristics of gene expression FISH + microscopyStained by FISH and imaged by.Imaging: the only way to record spatial location ++structural infoScanning across x+y to acquire optical sliceCan be visualized in projections (slices) or volume rendering! THEY R VOLUMETRIC IMAGES!! to view gene activity=> FISH, attaching a probe to a transcript!!FISH: the assay of choice for localization of specific nucleic acids sequences in native context. Basic principles remain unchanged, now a wide spectrum of detection schemesemission independent of absorption!! Here spectrum with Leica!“capture the relative spatial context of the fluorescently labelled structures”“spatial map of gene expression patterns”Definition of the channel: “ the image data from each fluorescent label” the two or three channels were acquired separately but simultaneously, as the emission spectrum is distinct.The dyes emit light in different parts of the spectrum, so that three separate images of the embryo can be collected with the appropriate color filters (middle)
  4. Tools for studying the spatial characteristics of gene expression FISH + microscopyStained by FISH and imaged by.Imaging: the only way to record spatial location ++structural infoScanning across x+y to acquire optical sliceCan be visualized in projections (slices) or volume rendering! THEY R VOLUMETRIC IMAGES!! to view gene activity=> FISH, attaching a probe to a transcript!!FISH: the assay of choice for localization of specific nucleic acids sequences in native context. Basic principles remain unchanged, now a wide spectrum of detection schemesemission independent of absorption!! Here spectrum with Leica!“capture the relative spatial context of the fluorescently labelled structures”“spatial map of gene expression patterns”Definition of the channel: “ the image data from each fluorescent label” the two or three channels were acquired separately but simultaneously, as the emission spectrum is distinct.The dyes emit light in different parts of the spectrum, so that three separate images of the embryo can be collected with the appropriate color filters (middle)
  5. Image registration is the process of determining the spatial transform that maps points from one image to homologous points in another.
  6. We r talking about the intensity based approach that works directly on the intensity values of the images and does not require any interaction from the user (while the registration is running).The basic input data are two images: one defined as fixed (static) and the other as moving, that will be spatially map to align with the first.Treated as an optimization problem with the goal of finding the spatial mapping that will bring the MOVING into alignment with the FIXED.
  7. We would expect the transformation parameters to map points from the moving to fixed. However this transformation could result in holes or overlaps. Therefore the transformation is done backwards.Inverse mapping= avoid holes, overlaps
  8. The intensities on the transformed grid are taken by interpolating values in the moving.
  9. After the transformation, the images are run thru a similarity measureFor large transformations (total misalignment) only background noise overlaps!The optimum of this function is assumed to correspond to the transformation that successfully registers the images
  10. Based on the Information Theory, that says that the amount of information they contain about each other is maximal.H entropies information they contain about themselves, joint entropy measures the dispersion of the joint probability distribution. The more they match, the clearer the clusters that can be seen on the joint histogram.
  11. The result of the similarity measure is given to ..Goal: is the component that drives the registration. It explores the parameter space of the T in search of a set of values that optimize the similarity measure function!!!!! This is an iterative procedure until reaches…Now a question here is whether we are looking for global or local of the similarity measure function. For intensity-based registration measures, it is possible that a large misregistration of two images results in a better value of the measure than the correct transformation. The desired optimum may not be the global one of the search space and only part of the search space leads to the desired optimum.
  12. GD: Advances parameters in the direction of the gradient where the step size is governed by a learning rate (λ)DE: It is an evolutionary algorithm. Starting from a population vector with size NP, it generates a mutant vector from the existing elements (f is just a weighting factor). To increase diversity of the population, crossing-over with a probability of CR is introduced to construct a trial vector. This trial vector is compared to the population vector and the elements that yield the best similarity measure values are passed on to the next generation (greedy criterion).(Gradient: we can choose the directionPopulation vector initial parameter values randomly from IPR, parameters for next generation vector selected according to the greedy criterion)One difference between them is that GD requires derivative of S whereas DE no!! Another is local, global
  13. That is function to be minimized…
  14. Stochastic, population-based
  15. Stochastic, population-based
  16. Stochastic, population-based
  17. Start the algorithm within the capture range of the desired optimum!Starting estimate of transformation close to the correct solution (initialization)
  18. The output is the transformation. That is given to ..Last step of the registration is to use the resulting transformation to map the moving image onto the fixed image SPACE!!Takes moving and parameters and produces the registered transformed image.
  19. Present yourself
  20. Because cellular resolution was required for this dataset, they cover limited views, restricted to the stained region.
  21. The pipeline that prepares the data before the actual registration, the pipeline that leads all the way to registration
  22. scalar parameter A balances the weight in the registrationprocess of nuclei structural information and gene expression details
  23. the better the images overlap before the registration step, the less displacement theregistration algorithm has to cover and more chances to obtain a useful alignment.
  24. We have already seen the components it is composed of. Here we can see what has been selected for each component.2 and 2 so we can compare their performance.
  25. Command line programsThe parameters under which each program is run can be configured by the user!!!!
  26. Present yourself
  27. “the volume that has been imaged encompasses part of the blastoderm (embryonic cell mass?) and does not get into the yolk (non cellular mass of nutrients)”DEPSN (Development Evolution Plasticity of the Nervous System), Francedatasets dapi, CY5, FITC, imaged by LSM
  28. Many qs concerning configuration of parameters of programs.
  29. Template always enters the algorithmNext comes the step of initialization
  30. (part of the transformation parameters space that includes the desired optimal value
  31. the combination of the correlation coefficient with the gradient descent algorithm presented a coherent and sufficient performance, when the division factor in the addition step was in the [1–6] rangeAlthough the in situ hybridizations of gene X and Y were conducted independently, their expression patterns can be visualized simultaneously.
  32. Present yourself
  33. End!