Sinha, Neelam and Ramakrishnan, AG (2003) Automation of Differential Blood Count. In: Conference on Convergent Technologies for Asia-Pacific Region TENCON 2003, 15-17 October, Bangalore,India, Vol.2, 547 -551.
A technique for automating the differential count of blood is presented. The proposed system takes, as input, color images of stained peripheral blood smears and identifies the class of each of the white blood cells (WBC), in order to determine the count of cells in each class. The process involves segmentation, feature extraction and classification. WBC segmentation is a two-step process carried out on the HSV-equivalent of the image, using k-means clustering followed by the EM-algorithm. Features extracted from the segmented cytoplasm and nucleus, are motivated by the visual cues of shape, color and texture. Various classifiers have been explored on different combinations of feature sets. The results presented are based on trials conducted with normal cells. For training the classifiers, a library set of 50 patterns, with about 10 samples from each class, is used. The test data, disjoint from the training set, consists of 34 patterns, fairly represented by every class. The best classification accuracy of 97% is obtained using neural networks, followed by 94% using SVM.
|Item Type:||Conference Paper|
|Additional Information:||Ã�Â©1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||Differential blood count;Cell segmentation;EM algorithm|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||26 Dec 2005|
|Last Modified:||19 Sep 2010 04:22|
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