SlideShare a Scribd company logo
1 of 63
Introduction to ITK Segmentation The Insight Consortium presented by Josh Cates Scientific Computing and Imaging Institute University of Utah
Session Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Important Facts about ITK Filters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Big Picture: The Role of ITK Filters Raw Data Filtering linear nonlinear Feature Extraction differential geom. edge detection Segmentation region growing watersheds level-sets Visualization binary volume meshes labeled image implicit surfaces Dataflow architecture: ITK filters fit together to produce segmentation applications. Preprocessing Moral: ITK segmentation filters  are not complete applications –  components in a pipeline.
Big Picture: The Role of ITK Filters Raw Data Filtering Feature Extraction Segmentation Visualization User Interface
Where to go to  really  learn to use the filters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What is an ITK Image? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],LargestPossibleRegion BufferedRegion RequestedRegion
Thresholding ,[object Object]
Thresholding ,[object Object]
Intensity Transformations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Intensity Transformations ,[object Object]
Image Morphology ,[object Object],[object Object]
Image Morphology ,[object Object],[object Object]
Edge Detection & Feature Extraction ,[object Object],[object Object],[object Object]
Edge Detection & Feature Extraction ,[object Object]
Edge Detection & Feature Extraction ,[object Object]
Image Denoising: Linear ,[object Object]
Image Denoising: Linear ,[object Object]
Image Denoising: Linear ,[object Object],[object Object],[object Object]
Image Denoising: Nonlinear ,[object Object],[object Object],[object Object],[object Object]
Image Denoising: Nonlinear ,[object Object],[object Object]
Linear Diffusion
Nonlinear Diffusion
Geometric Transformations ,[object Object]
Remember ,[object Object],[object Object],[object Object]
Image Neighborhood Framework ,[object Object],[object Object],[object Object],[object Object],[object Object]
Neighborhood Iterator Framework
Neighborhood Iterators
ITK Segmentation Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical Pattern Classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical Pattern Classification Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Membership Function(+) Classifier Decision Rule ,[object Object],[object Object],[object Object],[object Object],Parameter Estimator(+) Data Container(+) Training
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Classifier Framework Example *Courtesy of Dr. Lydia Ng, Allen Institute for Brain Science,  www.brainatlas.org .
Classifier Framework Example ,[object Object],[object Object],[object Object]
Region Growing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Confidence Connected Filter ,[object Object],[object Object],[object Object],Compute   and   of region Flood fill with threshold interval  k  k  Repeat N times
Region Growing Segmentation ,[object Object],smoothing iterations 5 smoothing time step 0.125 C.C. multiplier 2.5 C.C. iterations 5 white matter (60,116) original ventricle (81,112) gray matter (107,69)
Region Growing Segmentation ,[object Object]
Watershed Segmentation ,[object Object],[object Object],[object Object],[object Object],[object Object]
ITK Watershed Transform Image (filtered) Feature Extraction “ Edge Map” Watershed Transform Watershed Depth
The Oversegmentation Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Watersheds Hierarchy Watershed Depth Threshold Boolean Operations On Sub-trees (e.g. user interaction) Oversegmented Undersegmented = basin  Watershed Transform Watershed Depth Enforce minimum watershed depths at successively higher levels. Initial Watershed  Transform
Example: Watersheds GUI Watershed Depth Threshold InsightApplications/SegmentationEditor 3D isosurface rendering Data with overlay Watershed transform Segmentation in progress Sliders manipulate watershed depth and position in the hierarchy.
Example: Watersheds GUI
Example: Watersheds GUI
LevelSet Surface Modeling Theory ,[object Object],[object Object],[object Object]
Segmentation Using Level Sets ,[object Object],[object Object],[object Object]
PDE Solver Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PDE Solver Hierarchy Dense Finite Difference Solver Sparse Narrow Band Diffusion Other Solvers Finite Difference Function Diffusion Level Set Other Functions Aniso. Diff Curv.  Limited Segment. Threaded Sparse 4 th  Order Deformable Registration
Constructing a PDE Filter Input Image Output Image (Filtered) Solver Object Subclass Function Object Parameters
LevelSet Segmentation Framework “ Feature” Image Initial Model Output Model User-Defined LS Seg. Filter Level-Set Segmentation Filter Finite Difference Solver Curvature Function Finite Difference Solver Sparse-Field Level-Set Solver Level Set Function Shape Detection Function Active-Contours Function Laplacian Function Threshold Function Canny Edges Function
LevelSet Segmentation Algorithms in ITK ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Threshold based LS Segmentation Speed function (positive inside object) Similar to confidence connected filter Points Inside Points Outside Points Outside Low Threshold High Threshold Image Intensity Model Speed ,[object Object],[object Object]
Example: LevelSet Segmentation GUI
Multiscale LevelSet 3D Segmentation Seed surface Data Scale 1/4 1/2 1/1
Advanced Features in the PDE Framework ,[object Object],Speedup (vs. 1 processor Number of processors SGI Origin 3000 64 600 Mhz Processors
4 th  Order Flow Segmentation Framework
Segmentation Using 4 th  Order Flows ,[object Object],Speed term only Speed + Anisotropic 4 th  order terms (not real-time)
SNAP Tool ,[object Object],[object Object],[object Object],InsightApplications/Snap
SNAP User Interface Dialog for setting evolution parameters Scalpel tool for editing segmentations Manual Segmentation InsightApplications/Snap SNAP Segmentation of the Caudate Nuclei SNAP Segmentation Wizard with VCR Controls
“Hybrid” Segmentation Methods ,[object Object],[object Object],[object Object],[object Object]
Hybrid Method: Region Growing + Level Sets ,[object Object],[object Object],Canny LS Segmentation Filter LS Image (float) Image Confidence  Connected Initial  model Anisotropic Diffusion Feature Image
Confidence Connected + LevelSet Result Initial confidence- connected result Post-processing with  Canny LS segmenter LS Speed Term: distance from Canny edges Data: Warfield, Nabavi, Butz, Tuncali, Silverman, “Intraoperative segmentation and nonrigid  registration for image guided therapy, in: MICCAI'2000, SpringerVerlag, 2000, pp.176-185.
http://www.itk.org enjoy ITK!

More Related Content

What's hot

Video object tracking with classification and recognition of objects
Video object tracking with classification and recognition of objectsVideo object tracking with classification and recognition of objects
Video object tracking with classification and recognition of objects
Manish Khare
 
A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv...
 A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv... A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv...
A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv...
Chennai Networks
 

What's hot (20)

Video object tracking with classification and recognition of objects
Video object tracking with classification and recognition of objectsVideo object tracking with classification and recognition of objects
Video object tracking with classification and recognition of objects
 
Object tracking
Object trackingObject tracking
Object tracking
 
Object Detection & Tracking
Object Detection & TrackingObject Detection & Tracking
Object Detection & Tracking
 
Detection and Tracking of Moving Object: A Survey
Detection and Tracking of Moving Object: A SurveyDetection and Tracking of Moving Object: A Survey
Detection and Tracking of Moving Object: A Survey
 
Presentation of Visual Tracking
Presentation of Visual TrackingPresentation of Visual Tracking
Presentation of Visual Tracking
 
Real-time Object Tracking
Real-time Object TrackingReal-time Object Tracking
Real-time Object Tracking
 
Object tracking
Object trackingObject tracking
Object tracking
 
IRJET-Haar Classifier based Identification and Tracking of Moving Objects fro...
IRJET-Haar Classifier based Identification and Tracking of Moving Objects fro...IRJET-Haar Classifier based Identification and Tracking of Moving Objects fro...
IRJET-Haar Classifier based Identification and Tracking of Moving Objects fro...
 
Overview Of Video Object Tracking System
Overview Of Video Object Tracking SystemOverview Of Video Object Tracking System
Overview Of Video Object Tracking System
 
TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCES
TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCESTRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCES
TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCES
 
Kk3517971799
Kk3517971799Kk3517971799
Kk3517971799
 
motion and feature based person tracking in survillance videos
motion and feature based person tracking in survillance videosmotion and feature based person tracking in survillance videos
motion and feature based person tracking in survillance videos
 
Gesture Recognition using Principle Component Analysis & Viola-Jones Algorithm
Gesture Recognition using Principle Component Analysis &  Viola-Jones AlgorithmGesture Recognition using Principle Component Analysis &  Viola-Jones Algorithm
Gesture Recognition using Principle Component Analysis & Viola-Jones Algorithm
 
Occlusion and Abandoned Object Detection for Surveillance Applications
Occlusion and Abandoned Object Detection for Surveillance ApplicationsOcclusion and Abandoned Object Detection for Surveillance Applications
Occlusion and Abandoned Object Detection for Surveillance Applications
 
IRJET- Image based Approach for Indian Fake Note Detection by Dark Channe...
IRJET-  	  Image based Approach for Indian Fake Note Detection by Dark Channe...IRJET-  	  Image based Approach for Indian Fake Note Detection by Dark Channe...
IRJET- Image based Approach for Indian Fake Note Detection by Dark Channe...
 
Edge Detection Using Fuzzy Logic with Varied Inputs
Edge Detection Using Fuzzy Logic with Varied InputsEdge Detection Using Fuzzy Logic with Varied Inputs
Edge Detection Using Fuzzy Logic with Varied Inputs
 
A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv...
 A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv... A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv...
A Genetic Algorithm-Based Moving Object Detection For Real-Time Traffic Surv...
 
Video surveillance Moving object detection& tracking Chapter 1
Video surveillance Moving object detection& tracking Chapter 1 Video surveillance Moving object detection& tracking Chapter 1
Video surveillance Moving object detection& tracking Chapter 1
 
Performance Evaluation of Image Edge Detection Techniques
Performance Evaluation of Image Edge Detection Techniques Performance Evaluation of Image Edge Detection Techniques
Performance Evaluation of Image Edge Detection Techniques
 
lab report 4
lab report 4lab report 4
lab report 4
 

Similar to ITK Tutorial Presentation Slides-946

D3 D10 Unleashed New Features And Effects
D3 D10 Unleashed   New Features And EffectsD3 D10 Unleashed   New Features And Effects
D3 D10 Unleashed New Features And Effects
Thomas Goddard
 
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...
ijtsrd
 
Hardware Shaders
Hardware ShadersHardware Shaders
Hardware Shaders
gueste52f1b
 

Similar to ITK Tutorial Presentation Slides-946 (20)

D3 D10 Unleashed New Features And Effects
D3 D10 Unleashed   New Features And EffectsD3 D10 Unleashed   New Features And Effects
D3 D10 Unleashed New Features And Effects
 
IRJET - Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...
IRJET -  	  Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...IRJET -  	  Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...
IRJET - Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...
 
Thesis Giani UIC Slides EN
Thesis Giani UIC Slides ENThesis Giani UIC Slides EN
Thesis Giani UIC Slides EN
 
UIC Thesis Candiloro
UIC Thesis CandiloroUIC Thesis Candiloro
UIC Thesis Candiloro
 
GTC 2009 OpenGL Barthold
GTC 2009 OpenGL BartholdGTC 2009 OpenGL Barthold
GTC 2009 OpenGL Barthold
 
A CAD ppt 25-10-19.pdf
A CAD ppt 25-10-19.pdfA CAD ppt 25-10-19.pdf
A CAD ppt 25-10-19.pdf
 
Currency recognition on mobile phones
Currency recognition on mobile phonesCurrency recognition on mobile phones
Currency recognition on mobile phones
 
VLSI design flow.pptx
VLSI design flow.pptxVLSI design flow.pptx
VLSI design flow.pptx
 
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...
 
06_features_slides.pdf
06_features_slides.pdf06_features_slides.pdf
06_features_slides.pdf
 
Digital Image Processing
Digital Image ProcessingDigital Image Processing
Digital Image Processing
 
Feature Extraction and Feature Selection using Textual Analysis
Feature Extraction and Feature Selection using Textual AnalysisFeature Extraction and Feature Selection using Textual Analysis
Feature Extraction and Feature Selection using Textual Analysis
 
Hardware Shaders
Hardware ShadersHardware Shaders
Hardware Shaders
 
Computer Vision Landscape : Present and Future
Computer Vision Landscape : Present and FutureComputer Vision Landscape : Present and Future
Computer Vision Landscape : Present and Future
 
RAMSES: Robust Analytic Models for Science at Extreme Scales
RAMSES: Robust Analytic Models for Science at Extreme ScalesRAMSES: Robust Analytic Models for Science at Extreme Scales
RAMSES: Robust Analytic Models for Science at Extreme Scales
 
My Dissertation Presentation Slides
My Dissertation Presentation SlidesMy Dissertation Presentation Slides
My Dissertation Presentation Slides
 
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
An Efficient Frame Embedding Using Haar Wavelet Coefficients And Orthogonal C...
 
Presentation vision transformersppt.pptx
Presentation vision transformersppt.pptxPresentation vision transformersppt.pptx
Presentation vision transformersppt.pptx
 
Canny Edge Detection Algorithm on FPGA
Canny Edge Detection Algorithm on FPGA Canny Edge Detection Algorithm on FPGA
Canny Edge Detection Algorithm on FPGA
 
C010111519
C010111519C010111519
C010111519
 

More from Kitware Kitware

More from Kitware Kitware (20)

Radial Thickness Calculation and Visualization for Volumetric Layers-8397
Radial Thickness Calculation and Visualization for Volumetric Layers-8397Radial Thickness Calculation and Visualization for Volumetric Layers-8397
Radial Thickness Calculation and Visualization for Volumetric Layers-8397
 
Registration-3771
Registration-3771Registration-3771
Registration-3771
 
Automatic Brain Segmentation-3770
Automatic Brain Segmentation-3770Automatic Brain Segmentation-3770
Automatic Brain Segmentation-3770
 
Nrrd to Dicom Conversion-3769
Nrrd to Dicom Conversion-3769Nrrd to Dicom Conversion-3769
Nrrd to Dicom Conversion-3769
 
Data Saving-3767
Data Saving-3767Data Saving-3767
Data Saving-3767
 
FreeSurfer Reader-3766
FreeSurfer Reader-3766FreeSurfer Reader-3766
FreeSurfer Reader-3766
 
Functional Magnetic Resonance Imaging Analysis-3765
Functional Magnetic Resonance Imaging Analysis-3765Functional Magnetic Resonance Imaging Analysis-3765
Functional Magnetic Resonance Imaging Analysis-3765
 
Diffusion Tensor Imaging Analysis-3749
Diffusion Tensor Imaging Analysis-3749Diffusion Tensor Imaging Analysis-3749
Diffusion Tensor Imaging Analysis-3749
 
Manual Segmentation-3747
Manual Segmentation-3747Manual Segmentation-3747
Manual Segmentation-3747
 
Data Loading and Visualization-3735
Data Loading and Visualization-3735Data Loading and Visualization-3735
Data Loading and Visualization-3735
 
Principles and Practices of Scientific Originology-8392
Principles and Practices of Scientific Originology-8392Principles and Practices of Scientific Originology-8392
Principles and Practices of Scientific Originology-8392
 
Principles and Practices of Scientific Originology-8391
Principles and Practices of Scientific Originology-8391Principles and Practices of Scientific Originology-8391
Principles and Practices of Scientific Originology-8391
 
ITK Tutorial Presentation Slides-953
ITK Tutorial Presentation Slides-953ITK Tutorial Presentation Slides-953
ITK Tutorial Presentation Slides-953
 
ITK Tutorial Presentation Slides-952
ITK Tutorial Presentation Slides-952ITK Tutorial Presentation Slides-952
ITK Tutorial Presentation Slides-952
 
ITK Tutorial Presentation Slides-949
ITK Tutorial Presentation Slides-949ITK Tutorial Presentation Slides-949
ITK Tutorial Presentation Slides-949
 
ITK Tutorial Presentation Slides-948
ITK Tutorial Presentation Slides-948ITK Tutorial Presentation Slides-948
ITK Tutorial Presentation Slides-948
 
ITK Tutorial Presentation Slides-945
ITK Tutorial Presentation Slides-945ITK Tutorial Presentation Slides-945
ITK Tutorial Presentation Slides-945
 
ITK Tutorial Presentation Slides-944
ITK Tutorial Presentation Slides-944ITK Tutorial Presentation Slides-944
ITK Tutorial Presentation Slides-944
 
ITK Tutorial Presentation Slides-943
ITK Tutorial Presentation Slides-943ITK Tutorial Presentation Slides-943
ITK Tutorial Presentation Slides-943
 
A Quantitative DTI Fiber Tract Analysis Suite-898
A Quantitative DTI Fiber Tract Analysis Suite-898A Quantitative DTI Fiber Tract Analysis Suite-898
A Quantitative DTI Fiber Tract Analysis Suite-898
 

Recently uploaded

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Recently uploaded (20)

Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 

ITK Tutorial Presentation Slides-946

  • 1. Introduction to ITK Segmentation The Insight Consortium presented by Josh Cates Scientific Computing and Imaging Institute University of Utah
  • 2.
  • 3.
  • 4. Big Picture: The Role of ITK Filters Raw Data Filtering linear nonlinear Feature Extraction differential geom. edge detection Segmentation region growing watersheds level-sets Visualization binary volume meshes labeled image implicit surfaces Dataflow architecture: ITK filters fit together to produce segmentation applications. Preprocessing Moral: ITK segmentation filters are not complete applications – components in a pipeline.
  • 5. Big Picture: The Role of ITK Filters Raw Data Filtering Feature Extraction Segmentation Visualization User Interface
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 24.
  • 25.
  • 26.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39. ITK Watershed Transform Image (filtered) Feature Extraction “ Edge Map” Watershed Transform Watershed Depth
  • 40.
  • 41. Watersheds Hierarchy Watershed Depth Threshold Boolean Operations On Sub-trees (e.g. user interaction) Oversegmented Undersegmented = basin Watershed Transform Watershed Depth Enforce minimum watershed depths at successively higher levels. Initial Watershed Transform
  • 42. Example: Watersheds GUI Watershed Depth Threshold InsightApplications/SegmentationEditor 3D isosurface rendering Data with overlay Watershed transform Segmentation in progress Sliders manipulate watershed depth and position in the hierarchy.
  • 45.
  • 46.
  • 47.
  • 48. PDE Solver Hierarchy Dense Finite Difference Solver Sparse Narrow Band Diffusion Other Solvers Finite Difference Function Diffusion Level Set Other Functions Aniso. Diff Curv. Limited Segment. Threaded Sparse 4 th Order Deformable Registration
  • 49. Constructing a PDE Filter Input Image Output Image (Filtered) Solver Object Subclass Function Object Parameters
  • 50. LevelSet Segmentation Framework “ Feature” Image Initial Model Output Model User-Defined LS Seg. Filter Level-Set Segmentation Filter Finite Difference Solver Curvature Function Finite Difference Solver Sparse-Field Level-Set Solver Level Set Function Shape Detection Function Active-Contours Function Laplacian Function Threshold Function Canny Edges Function
  • 51.
  • 52.
  • 54. Multiscale LevelSet 3D Segmentation Seed surface Data Scale 1/4 1/2 1/1
  • 55.
  • 56. 4 th Order Flow Segmentation Framework
  • 57.
  • 58.
  • 59. SNAP User Interface Dialog for setting evolution parameters Scalpel tool for editing segmentations Manual Segmentation InsightApplications/Snap SNAP Segmentation of the Caudate Nuclei SNAP Segmentation Wizard with VCR Controls
  • 60.
  • 61.
  • 62. Confidence Connected + LevelSet Result Initial confidence- connected result Post-processing with Canny LS segmenter LS Speed Term: distance from Canny edges Data: Warfield, Nabavi, Butz, Tuncali, Silverman, “Intraoperative segmentation and nonrigid registration for image guided therapy, in: MICCAI'2000, SpringerVerlag, 2000, pp.176-185.