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A Study on Segmentation of Leukocyte Image With Shannon's Entropy

A Study on Segmentation of Leukocyte Image With Shannon's Entropy
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Author(s): N. Sri Madhava Raja (St. Joseph's College of Engineering, India), S. Arunmozhi (Manakula Vinayagar Institute of Technology, India), Hong Lin (University of Houston – Downtown, USA), Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India) and V. Rajinikanth (St. Joseph's College of Engineering, India)
Copyright: 2019
Pages: 27
Source title: Histopathological Image Analysis in Medical Decision Making
Source Author(s)/Editor(s): Nilanjan Dey (Techno India College of Technology, India), Amira S. Ashour (Tanta University, Egypt), Harihar Kalia (Seemantha Engineering College, India), R.T. Goswami (Techno India College of Technology, India) and Himansu Das (KIIT University, India)
DOI: 10.4018/978-1-5225-6316-7.ch001


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In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.

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