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Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

Vanika Singhal, Preety Singh
Copyright: © 2018 |Pages: 22
ISBN13: 9781522528296|ISBN10: 1522528296|EISBN13: 9781522528302
DOI: 10.4018/978-1-5225-2829-6.ch009
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MLA

Singhal, Vanika, and Preety Singh. "Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia." Biomedical Signal and Image Processing in Patient Care, edited by Maheshkumar H. Kolekar and Vinod Kumar, IGI Global, 2018, pp. 162-183. https://doi.org/10.4018/978-1-5225-2829-6.ch009

APA

Singhal, V. & Singh, P. (2018). Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia. In M. Kolekar & V. Kumar (Eds.), Biomedical Signal and Image Processing in Patient Care (pp. 162-183). IGI Global. https://doi.org/10.4018/978-1-5225-2829-6.ch009

Chicago

Singhal, Vanika, and Preety Singh. "Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia." In Biomedical Signal and Image Processing in Patient Care, edited by Maheshkumar H. Kolekar and Vinod Kumar, 162-183. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2829-6.ch009

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Abstract

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

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