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CS 229 Project Milestone Report : Automated Segmentation of Breast Density

This project is to develop an automated breast density segmentation system. Breast density has been shown to be a key indicator of breast cancer risk. Currently the primary computational system for analyzing breast density is a semiautomated segmentation system that requires a radiologist to set thresholds for the segmentation algorithm. This results in reader variability, which reduces the validity of the density assessment. I propose a fully automated segmentation system. The system will utilize superpixels and neighborhood information to create visual dictionaries from training data and use this information to segment image patches (small superpixels) as dense or not dense.