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

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.