Data: Original Images

This contains two data sets (MGH - Training and BIDMC - Evaluation)

Code: Texture Features Computation

This is C++ code that computes intensity and texture features of each segmented nuclei. This code requires ITK 4 or above version and Boost library to compile and run. It also requires a library of color transformation into different color spaces which you can find at this link (https://github.com/midas-journal/midas-journal-780.git)

File: Selected Features List

This file contains a list of selected features

File: Breast Cancer Cases (UDH & DCIS)

This file contains a list of all cases with clinical data that used for class labelling

Fig: Analysis Figures

This contains analysis figures that describes the framework performance on data sets

Data: Original and Segmented Images

This contains both original and segmented images

Code: Nuclei Segmentation & Morphological Features

This contains a Fiji (ImageJ) Macro that segment nuclei and compute morphological Features

Features and weights in the DCIS vs. UDH classification model.

<p>Features and weights in the DCIS vs. UDH classification model.</p

Classification performance for DCIS and UDH classification models across a range of classification tasks and using varying subsets of features.

<p>Classification performance for DCIS and UDH classification models across a range of classification tasks and using varying subsets of features.</p