Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes

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Published: Jun 3, 2020
Keywords:
Histogram of oriented gradient K-means clustering YCbCr color space WBC classification Microscopic blood smear image

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Anas Mohd Noor
School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia
Haniza Yazid
School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia
Zulkarnay Zakaria
School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia
Aishah Mohd Noor
Institute of Engineering Mathematics, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia

Abstract

Researchers developed various methods and algorithms to classify white blood cells (WBCs) from blood smear images to assist hematologists and to develop an automatic system. Furthermore, the pathological and hematological conditions of WBCs are related to diseases that can be analyzed accurately in a short time. In this work, we proposed a simple technique for WBC classification from a peripheral blood smear image based on the types of cell nuclei. The developed algorithms utilized a histogram of oriented gradient (HOG) feature typically known for application in human disease detection. The segmentation of WBC nuclei utilizes a YCbCr color space and K-means clustering techniques. The HOG feature contains information about the cell nuclei shapes, which then is classified using a support vector machine (SVM) and backpropagation artificial neural network (ANN). The results show that the proposed HOG feature is useful for WBC classification based on the shapes of nuclei. We are able to categorize the type of a WBC based on its nucleus shape with more than 95% accuracy.

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How to Cite
Mohd Noor, A., Yazid, H. ., Zakaria, Z. ., & Mohd Noor, A. . (2020). Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes. Engineering and Applied Science Research, 47(2), 129–136. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/207976
Issue
Vol. 47 No. 2 (2020)
Section
ORIGINAL RESEARCH

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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