1. Lijuan Zhao Advisors: Prof. Fatima Merchant Prof. Shishir Shah

2. OUTLINE Motivation Computational Problem Challenges Literature Review Future Work

3. Motivation Breast 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

4. 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

5. Computational Problem Example of 3D torso image Point cloud Triangular mesh surface 2D texture image mapped onto surface

6. Computational Problem (cont’d) Visit 1Visit 2Visit 3 Retrieve breast data from 3D torso images Analyze breast changes for different visits for same patient

7. Challenges Chest walls are not matched for different visits - Coordinate systems may not be same - Patient weight change - 3D corresponding are required

8. Challenges (cont’d) Manually retrieve data may change points coordinates The transformations of the breast data are non-rigid

9. 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

10. 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

11. Future Work Step 1: chest wall calibration - Choose some fiducial points and connect them - Choose same points on different images - Construct the mathematical model

12. 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

13. 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 sets divergence measure deformable model