multigenerational analysis and visualization of large 3d vascular images
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Multigenerational Analysis And Visualization of Large 3D Vascular Images. Shu-Yen Wan Department of Information Management, Chang Gung University, Taiwan, R.O.C. Erik L. Ritman Department of Physiology, Mayo Clinic Foundation, USA William Higgins

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multigenerational analysis and visualization of large 3d vascular images

Multigenerational Analysis And Visualization of Large 3D Vascular Images

Shu-Yen Wan

Department of Information Management, Chang Gung University, Taiwan, R.O.C.

Erik L. Ritman

Department of Physiology, Mayo Clinic Foundation, USA

William Higgins

Department of Electrical Engineering, Pennsylvania State University, USA

SPIE Medical Imaging 2001: Image Processing, San Diego, CA, USA, Feb 19-22, 2001

problem statement
Problem Statement

Extract embedded geometrical information in large 3D medical images containing vascular networks to perform physiological studies and identify the abnormal.

Analysis

  • Branching Geometric Information
  • Retrieval of Analysis Statistics
  • Vasculature manipulation
  • Guided Navigation for Education and Surgery

visualization

challenges
Challenges
  • Control1
  • Size: 80.2 MB (16-bit)
  • Subject: rat liver, bile ducts
  • Dimensions: 399215491
  • Voxel resolution: x=y=z=21m
  • Manual analysis is time-consuming
  • Automatic analysis requires considerable
    • storage & processing space
    • computation
  • Efficient network representation is critical
  • Root identification
  • 3D Visualization to interact with extracted information
  • Control2
  • Size: 114.4 MB (16-bit)
  • Subject: rat liver, bile ducts
  • Dimensions: 400400375
  • Voxel resolution: x=y=z=21m
  • Control3
  • Size: 73 MB (16-bit)
  • Subject: rat liver, bile ducts
  • Dimensions: 319247487
  • Voxel resolution: x=y=z=21m
specific aims
Specific Aims
  • Devise efficient individual algorithms and integrated analysis procedure for large 3D branching networks.
  • Construct visualization tools to interact with the extracted image information.
  • Validation.
analysis procedure for 3d vascular images

Extraction of Regions of Interest

3D

Sigma

Filter

3D Symmetric

Region Growing

Segmentation

3D Cavity

Deletion

Extracted Trees

Vasculature Representation and Analysis

  • 3D Thinning

Vasculature

Representation

Measurement

Calculation

Root

Identification

Pruning

Analysis Procedure for 3D Vascular Images
branching analysis of control1

More analysis not shown

Branching Analysis of Control1 – an excerpt

Branching Analysis of Control1
volume loss analysis
Volume Loss Analysis

1-(D3d1/D3m+D3d2/D3m) = a

problems with traditional image viewing
Problems With Traditional Image Viewing
  • Only Two-Dimensional Views
  • Restricted Viewing Directions
  • Can’t Retrieve Extracted and Computed Geometrical Information
  • Lack of Global Views of the Vasculature
visualization of geometrical information
Visualization of Geometrical Information
  • Determine intensity range of regions of interest
  • Estimate root location of the branching structure
  • Display quantitative analysis results
  • Dynamically interact with the extracted vasculature
  • 3D Viewers
    • 3D Slicer Tools
    • 3D-to-2D Projection Tools
  • 3D Tree Tool
3d viewer

2D Slicer Tools

3D Viewer

histogram equalization

color table selection

point of selection and its gray-level value

color table entry# 0

color table entry# 255

3D-2D Projection Tools

summary and future work
Summary and Future Work
  • 3D image processing and analysis procedure
  • Perform comprehensive generational analysis on networks contained in 3D images
  • Visualization tools
    • Offer global view of extracted branching structures
    • Display branching geometric information
    • Interactively edits of the branching structures
  • Java implementation of the analysis system
  • Studies on Medical Education and Image-Guided Surgery
  • Improvements on the 3D Rendering Tools.
thank you
Thank You!

Thank you !

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