Enhancement K-Mean Algorithm (EKM) for Image Processing in MATLAB

Himanshu Monga

International Journal of Image Processing and Pattern Recognition

Enhancement K-Mean Algorithm (EKM) for Image Processing in MATLAB

Himanshu Monga ✉

Abstract

In this paper, a new segmentation scheme for the white blood cells from microscopic images is proposed. This is based on the K-means clustering technique. The RGB test images are converted to the L*a*b color space, and then the two color machinery (a and b) are used as features to the K-means clustering algorithm. The success of image analysis depends on segmentation reliability. The accurate partition of the image into regions is a challenging task. K-Means Clustering algorithm is the popular unsupervised clustering for dividing the images into multiple regions based on image color property. The major feature of the algorithm is that the user can specify the number of clusters- K, which is used to split the image into K regions.

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Enhancement K-Mean Algorithm (EKM) for Image Processing in MATLAB

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