3d analysis of breast changes for medical images
DESCRIPTION
3D Analysis of Breast Changes for Medical Images. Lijuan Zhao Advisors: Prof. Fatima Merchant Prof. Shishir Shah. OUTLINE. Motivation Computational Problem Challenges Literature Review Future Work. Motivation. Breast Reconstruction - PowerPoint PPT PresentationTRANSCRIPT
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3D Analysis of Breast Changes for Medical Images
Lijuan Zhao
Advisors: Prof. Fatima Merchant Prof. Shishir Shah
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OUTLINEMotivationComputational ProblemChallenges Literature ReviewFuture Work
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MotivationBreast Reconstruction
- Breast cancer is the most life-threatening disease in women - Breast cancer treatments usually lead to complete or
partial breast removal- Breast reconstruction can help breast cancer survivors
regain their quality of life
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Motivation (cont’d)Measurements of breast aesthetics
- Volume, symmetry, ptosis, projection, etc- Limitations: only estimate surgical results
unable to give guidance for surgery
Analysis of change for each point on breast- Better evaluation of surgical outcomes - Provide guidance for surgery
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Computational ProblemExample of 3D torso image
Point cloud Triangular mesh surface
2D texture imagemapped onto surface
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Computational Problem (cont’d)Visit 1 Visit 2 Visit 3
Retrieve breast data from 3D torso imagesAnalyze breast changes for different visits for same patient
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ChallengesChest walls are not matched for different visits
- Coordinate systems may not be same- Patient weight change - 3D corresponding are required
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Challenges (cont’d)Manually retrieve data may change points coordinates
The transformations of the breast data are non-rigid
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Literature Review (1) Robust point set registration using Gaussian mixture
models Using Gaussian mixture models to represent point sets Divergence measure: L2 distance Deformation model: thin-plate splines (TPS)+ gaussian radial basis
functions (GRBF) Cost function: PROS: efficient and robust CONS: only works for pair-wise point set
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Literature Review (cont’d)(2) Group-wise point-set registration using a novel CDF-based
Havrda-Charvat divergence Using Dirac mixture models to represent point sets Divergence measure: CDF-HC divergence Deformation model: thin-plate splines (TPS) Cost function: PROS: efficient and simple to implement; works for group-wise point sets CONS: not robust for noise and outliers
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Future WorkStep 1: chest wall calibration
- Choose some fiducial points and connect them- Choose same points on different images- Construct the mathematical model
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Future Work (cont’d)Step 2: automatically retrieve the breast data
- Based on mathematical model, calculate the corresponding coordinates for points on chest wall
- Using curvature property retrieve the breast data
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Future Work (cont’d)Step 3: using 3D group-wise point sets non-rigid
registration to analyze breast changes. - Down sampling point cloud (if necessary)VTK
- Propose new method with good cost function and optimization scheme suitable model to represent point setsdivergence measuredeformable model