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

Authors

  • Kazeem Oyeyemi Kazeem University of KwaZulu-Natal

Keywords:

Segmentation, Otsu Thresholding, Cell images, Cell Segmentation,

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%).

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Published

2018-01-15

How to Cite

Kazeem, K. O. (2018). Improved Thresholding Method for Cell Image Segmentation Based on Global Homogeneity Information. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1), 13–16. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1732

Issue

Vol. 10 No. 1: January - March 2018

Section

Articles

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