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Analysis of Blood Smear and Detection of White Blood Cell Types Using Harris Corner
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Author(s): Nilanjan Dey (JIS College of Engineering, India), Bijurika Nandi (CIEM, Tollygunge, India), Anamitra Bardhan Roy (JIS College of Engineering, India), Debalina Biswas (JIS College of Engineering, India), Achintya Das (Kalyani Government Engineering College, India)and Sheli Sinha Chaudhuri (Jadavpur University, India)
Copyright: 2014
Pages: 14
Source title:
Research Developments in Computer Vision and Image Processing: Methodologies and Applications
Source Author(s)/Editor(s): Rajeev Srivastava (Indian Institute of Technology (BHU), India), S. K. Singh (Indian Institute of Technology (BHU), India)and K. K. Shukla (Indian Institute of Technology (BHU), India)
DOI: 10.4018/978-1-4666-4558-5.ch017
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Abstract
Blood cell smears contain huge amounts of information about the state of human health. This chapter proposes a Fuzzy c-means segmentation based method for the evaluation of blood cells of humans by counting the presence of Red Blood Cells (RBCs) and recognizing White Blood Cell (WBC) types using Harris corner detection. Until now hematologists gave major priority to WBCs and spent most of the time studying their features to reveal various characteristics of numerous diseases. Firstly, this method detects and counts the RBCs present in the human blood sample. Secondly, it assesses the detected WBCs to minutely scrutinize its type. It is a promising strategy for the diagnosis of diseases. It is a very tedious task for pathologists to identify and treat diseases by manually detecting, counting, and segmenting RBCs and WBCs. Simultaneously the analysis of the size, shape, and texture of every WBC and its elements is a very cumbersome process that makes this system vulnerable to inaccuracy and generates trouble. Hence, this system delivers a precise methodology to extract all relevant information for medical diagnosis with high germaneness maintaining pertinence. This present work proposes an algorithm for the detection of RBCs comparing the results between expert ophthalmologists’ hand-drawn ground-truths and the RBCs detected image as an output. Accuracy is used to evaluate overall performance. It is found that this work detects RBCs successfully with accuracy of 82.37%.
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