Segmentation of nuclei from breast histopathology images using PSO-based Otsu’s multilevel thresholding

Abstract

Automated histopathology image analysis involves segmentation of nuclei from the surrounding tissue structures to develop a computer-aided diagnosis (CAD) system. In this paper, we propose the use of particle swarm optimization (PSO)-based Otsu’s multilevel thresholding technique to automatically segment the nuclei from hematoxylin and eosin (H&E)-stained breast histopathology images. Otsu’s threshold selection problem is modeled as an optimization problem by designating the discriminant criterion as the objective or fitness function that has to be maximized. PSO is used to compute the optimal threshold value that maximizes the objective function. This paper studies the effectiveness of the proposed technique to segment nuclei from breast histopathology images.