Title: Colour thresholding-based automatic Ki67 counting procedure for immunohistochemical staining in meningioma

Authors: Fahmi Akmal Dzulkifli; Mohd Yusoff Mashor; Hasnan Jaafar

Addresses: School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia ' School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia ' Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia

Abstract: Nuclei segmentation is the initial process in the histopathological image analysis. This process plays a vital role in cell counting. The Ki67 is a nuclear that was widely used among the pathologists to measure the tumour cell proliferation. Generally, the pathologists use the manual counting technique for counting the Ki67 cells. However, the counting results have poor reliability and lack of accuracy. The current study aimed to propose an automatic Ki67 cell counting for meningioma images by using the colour thresholding approach. The proposed method has been tested on 12 photomicrographs of meningiomas. The results showed that the proposed method was able to segment the positive and negative Ki67 cells with an average accuracy of more than 90%. For counting results, the proposed system produced good results in counting the Ki67 cells with an average relative accuracy of 0.91 for positive Ki67 cells and 0.89 for negative cells.

Keywords: automated counting; colour thresholding; image segmentation; immunohistochemical staining; Ki67 cell; meningioma.

DOI: 10.1504/IJCVR.2021.115160

International Journal of Computational Vision and Robotics, 2021 Vol.11 No.3, pp.279 - 298

Received: 28 Jan 2019
Accepted: 03 Nov 2019

Published online: 06 Apr 2021 *

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