Figure 2

Building and evaluating a shape-based diagnostic model using histological images. We use three steps to derive a shape-based diagnostic model from histological images: 1) shape-based feature extraction (including automatic color segmentation, individual shape descriptor extraction, and discretization), 2) feature selection using the minimum redundancy-maximum relevance (mRMR) method, and 3) classifier model selection using cross-validation to identify optimal model parameters (i.e., feature size, Fourier shape descriptor harmonics, and SVM parameters). We evaluate the selected features and the classifier model by examining the biological relevance of the top selected features and by classifying independent images (using nested cross-validation).