BREAST CANCER GRADING OF H&E STAINED HISTOPATHOLOGY IMAGES

Abstract

Breast cancer is the common existing form of cancers amongst women. The automatic image analysis methods have an enormous potential to decrease the workload in a pathology laboratory. The grading of breast cancer histopathology images is used to find the level of breast cancer. The automatic grading of breast cancer histopathology images is a challenging task. In this paper a system for automatic detection of breast cancer grading of H&E stained histopathological images is presented. An image processing techniques such as preprocessing, segmentation, feature extraction and classification are used in this system. The segmentation of nuclei in H&E stained image is performed using color thresholding and maximum entropy thresholding. The features are computed according to Bloom Richardson grading criteria. The decision tree classifier is used to classify input image into three group i.e., low grade, intermediate grade and high grade.