3D Analysis of Breast Changes for Medical Images

Lijuan Zhao

Advisors: Prof. Fatima Merchant Prof. Shishir Shah

OUTLINEMotivationComputational ProblemChallenges Literature ReviewFuture Work

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

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

Computational ProblemExample of 3D torso image

Point cloud Triangular mesh surface

2D texture imagemapped onto surface

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

ChallengesChest walls are not matched for different visits

- Coordinate systems may not be same- Patient weight change - 3D corresponding are required

Challenges (cont’d)Manually retrieve data may change points coordinates

The transformations of the breast data are non-rigid

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

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

Future WorkStep 1: chest wall calibration

- Choose some fiducial points and connect them- Choose same points on different images- Construct the mathematical model

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

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