Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 6, Pages: 1-6
J. Puttmade Gowda1 and S. C. Prasanna Kumar2
1Department of Electronics Engineering, Jain University, #44/4, District Fund Road, Jayanagar, 9th Block, Behind Big Bazaar, Bengaluru - 560069, Karnataka, India; [email protected] 2Department of Electronics and Instrumentation, R V College of Engineering, Mysuru Road, R. V. Vidyanikethan Post, Bengaluru - 560059, Karnataka, India; [email protected]
Testing of blood is a very important examination process, counting of cells is an important laboratory process for identifying blood related diseases. Microscopic evaluation by experts is a slow process and result is depends on skill and experience of technician, also the process is tedious and time consuming. Therefore automatic medical diagnosis system is necessary way to identifying the diseases in short time. For providing information about blood related diseases like leukemia it is necessary to identify and inspect white blood cell in a peripheral blood smear. So Segmentation is an important step in classifying the constituents of blood. This paper represents efficient segmentation of blood image by using K-means clustering method followed by Gram-Schmidt Orthogonal process to detect automatic blood cell nuclei.
Keywords: k-Means Clustering, Orthogonal Set, Orthonormal Set, Segmentation, White Blood Cell
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