An improved hybrid active contour model for nuclear segmentation on breast cancer histopathology

Abstract

Segmentation of nuclei on breast cancer histopathological images is considered a basic and essential step for diagnosis in a computer-aided diagnosis framework. Nuclear segmentation remains a challenging problem due to the inherent diversity of cancer biology and the variability of the tissue appearance. We present an automatic nuclear segmentation method using an improved hybrid active contour (AC) model driven by both boundary and region information. The initialization of segmentation based on morphological operations and watershed allows for generation of initial closed curves and reduction in computational load of curve evolution for the AC model. Color gradients are computed to capture image gradients along the margin of nucleus. The AC segmentation scheme is performed in a coarse-to-fine fashion which can help to solve the problem of multiple object overlap in an image scene. Segmentation performance was evaluated on various breast cancer histopathological images with different grades and was compared with the existing popular AC models, suggesting that our improved hybrid active contour model can be used to build an accurate and robust nuclear segmentation tool.