A classification of chronic leukaemia using new extension of k-means clustering and EFMM based on digital microscopic blood images. (2017)

• Record Type: Journal Article
• Authors: Kalaiselvi, C.; Asokan, R.
• Is Part Of: International journal of biomedical engineering and technology. Volume 23:Number 2/4(2017)
• Page Start: 232
• Page End: 241
• Publication Date: 2017
• Subjects: pre-processing -- k-means clustering -- Hausdorff dimension -- EFMM neural networks -- classification -- chronic leukaemia -- digital images -- medical images -- microscopic blood images -- colour scale -- image segmentation -- feature extraction -- enhanced fuzzy min max -- lymphocytic cells -- myelogenous cells -- white blood cells; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

Abstract

Leukaemia is a cancer of the white blood cells. The type of white blood cell affected in either lymphoid or myeloid. And leukaemia is defined in two ways, such as acute leukaemia (AL) and chronic leukaemia (CL). These kinds of leukaemia start when typical blood cells change and grow wildly. This paper describes in the following steps to classify the chronic leukaemia automatically and more accurately. First, pre-processing the colour scale of digital microscope blood image, then segment the image by new extension of k-means clustering algorithm, and Hausdorff dimension (HD) is utilised for feature extraction, finally the classification is done by utilising Enhanced Fuzzy Min Max (EFMM) neural network. The proposed method obtained 99.95% accuracy for Lymphocytic and Myelogenous cells.