A grading strategy for nuclear pleomorphism in histopathological breast cancer images using a bag of features (BOF)

Abstract

Nuclear pleomorphism is an early breast cancer (BCa) indicator that assesses any nuclear size, shape or chromatin appearance variations. Research involving the ranking by several experts shows that kappa coefficient ranges from 0.3(low) to 0.5 (moderate)[12]. In this work, an automatic grading approach for nuclear pleomorphism is proposed. First, a large nuclei sample is characterized by a multi-scale descriptor that is then assigned to the most similar atom of a previously learned dictionary. An occurrence histogram represents then any Field of View (FoV) in terms of the occurrence of the descriptors with respect to the learned atoms of the dictionary. Finally, a SVM classifier assigns a full pleomorphism grading, between 1 and 3, using the previous histogram. The strategy was evaluated extracting 134 FoV (×20), graded by a pathologist, from 14 BCa slides of ’The Cancer Genome Atlas’ (TCGA) database.The obtained precision and recall measures were 0.67 and 0.67.