Multifeature‐based medical image segmentation

Abstract

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. This study presents a local feature-based novel technique for the segmentation of histopathology images. It mainly focuses on a system that segments overlapped nuclei (OLN) without affecting the general non-OLN segmentation performance. The proposed method suggests a three-stage system. The initial segmentation is done by using local features for the demarcation of nuclei regions. In the second stage, salient-based active contour is applied for complete nucleus-region identification. In the final step, the OLN are identified and segmented using a Gaussian distribution and entropy maximisation. The performance of the proposed segmentation method is evaluated on the basis of precision, recall, accuracy, and F 1 -score. The proposed method is simulated on animal diagnostics laboratory histopathology image dataset and reported 90.3% average accuracy with average F 1-score 0.937. Simulation results confirm the superiority of the proposed method as compared with the existing state-of-art methods.