Abstract
The k-means clustering and Otsu-based thresholding of MRI images segmentation are widely used to cluster the lesions in human brain. The main objective of this paper is to employ both algorithms concept to obtain the optimum value of clusters center and threshold levels for a better segmentation process. Both segmentation approaches were used to partition the images into separate classes which are composed of pixels that have similar pre-defined feature values. The evaluation of both segmentation techniques were measured via qualitative and quantitative analysis. From the analysis of the results, it is justified that the proposed approaches are able to efficiently illustrate good segmentation results. The K-means algorithm is also successfully preserved important features of the MRI segmented images as the larger number of clustering reveals bigger grayscale intensity distribution on delineation marks of the MS lesions.
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