A classification of chronic leukaemia using new extension of k-means clustering and EFMM based on digital microscopic blood images Online publication date: Sat, 04-Mar-2017
by C. Kalaiselvi; R. Asokan
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 23, No. 2/3/4, 2017
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.
Online publication date: Sat, 04-Mar-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com