A Review on Computer-assisted Techniques to Analyze Histopathological Images of the Breast

This article provides an overview of computer-assisted techniques (CAT) used to assess histopathological images for Breast cancer. The Histopathological images analysis (HIPA) is time consuming and challenging. The shape, size, color and crowding of nuclei disclose important statistics about the tissue health. For diagnosis of cancer, nuclei are an important attribute which has to be isolated from other healthy tissues followed by feature extraction which is the primary and the most critical step. In order to distinguish between tumor or cancer types, experts assess morphometric features of each and every cell and their nuclei. The most familiar steps in histopathological image analysis like stain normalization, segmentation, feature extraction and classification are covered in this review. The segmentation methods of H & E images and several challenges related to stain normalization are discussed. Since stain normalization is a stage in the pre-processing of H & E images and the first step of feature extraction. In a nutshell, this article will outline different methods for analysing breast pathology images.