Improved Thresholding Method for Cell Image Segmentation Based on Global Homogeneity Information

Abstract

Cell segmentation provides opportunity to highlight abnormalities in the human body with a view to assist medical experts to diagnose objectively. In order to achieve this, a robust segmentation tool that gives high segmentation accuracy is desirable. Cell images can be classified as homogeneous and heterogeneous. Their existence in any of the two categories is a function of how they are captured. This however hinders the deployment of existing segmentation models such as graph cut, Otsu thresholding, k-means and watershed to cater for these categories of cell images. Our contribution in this paper is to develop in the first instance a segmentation model that automatically categorizes cell images as homogeneous and heterogeneous. Secondly, based on a category, a suitable and improved Otsu thresholding method is proposed for cell segmentation. Experimental results on heterogeneous cell images show improved segmentation accuracy of 91.36% over that derived from traditional Otsu thresholding (74%).