A cascaded classification-segmentation reversible system for computer-aided detection and cells counting in microscopic peripheral blood smear basophils and eosinophils images

Ashour, A. S., Wahba, M. A. and Ghannam, R. (2021) A cascaded classification-segmentation reversible system for computer-aided detection and cells counting in microscopic peripheral blood smear basophils and eosinophils images. IEEE Access, 9, pp. 78883-78901. (doi: 10.1109/ACCESS.2021.3083703)

Abstract

Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ghannam, Professor Rami
• Authors: Ashour, A. S., Wahba, M. A., and Ghannam, R.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Access
• Publisher: IEEE
• ISSN: 2169-3536
• ISSN (Online): 2169-3536
• Published Online: 25 May 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in IEEE Access 9: 78883-78901
• Publisher Policy: Reproduced under a Creative Commons License