Segmentation of White Blood Cell using K-Means and Gram-Schmidt Orthogonalization

J  Puttmade Gowda  and S  C  Prasanna Kumar

doi:10.17485/ijst/2017/v10i6/111137

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Segmentation of White Blood Cell using K-Means and Gram-Schmidt Orthogonalization

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2017/v10i6/111137

Year: 2017, Volume: 10, Issue: 6, Pages: 1-6

Original Article

Segmentation of White Blood Cell using K-Means and Gram-Schmidt Orthogonalization

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]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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