A robust leukocyte recognition method based on multi-scale regional growth and mean-shift clustering. Issue 3 (September 2018)

• Record Type: Journal Article
• Authors: Lin, Liqun; Wang, Weixing
• Is Part Of: Journal of algorithms & computational technology. Volume 12:Issue 3(2018)
• Page Start: 208
• Page End: 216
• Publication Date: 2018-09
• Subjects: Feature extraction -- leukocyte recognition -- mean-shift -- multi-scale regional growth -- SVM; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301818770839
• Languages: English
• ISSNs: 1748-3018

Abstract

Although there are many independent studies on the detection of white blood cell or classification of white blood cell, few papers have taken them into consideration. This study proposed a method for recognizing five types of leukocytes based on multi-scale regional growth and mean-shift clustering. The key idea of the proposed method is to extract texture features of leukocytes in a visual manner. And it is a non-parametric texture features extracting method different from traditional algorithms. Finally, SVM (Support Vector Machine) is used for classification. Some leukocyte images were used and the overall correct recognition rate reached 97.96%, indicating the feasibility and robustness of the proposed method.