Brain Tumor Extraction by K-Means Clustering Based On Morphological Image Processing (International Journal of Engineering and Computer Science, 2014)
Image processing is one of most growing analysis space today and currently it is much integrated with the medical and biotechnology field. Image processing will be used to analyze completely different medical and MRI images to get the abnormality within the image. Using mutual information as a criterion for medical image registration, which requires no prior segmentation or preprocessing, has been both theoretically and practically proved to be an effective method in these years. However, this technique is confined in registering two images and hard to apply to multiple ones. The reason is that unlike mutual information between two variables, high dimensional mutual information is ill defined. This paper proposes associate degree economical K-Means clustering algorithmic rule beneath Morphological Image Processing (MIP). Medical Image segmentation deals with segmentation of growth in CT and MR images for improved quality in diagnosis. It is a very important method and a difficult drawback because of noise presence in input images throughout image analysis. It’s required for applications involving estimation of the boundary of associate degree object, classification of tissue abnormalities, form analysis, contour detection. Segmentation determines because the method of dividing a image into disjoint invaried regions of a medical image. The quantity of resources needed to explain large set of knowledge is simplified and is chosen for tissue segmentation. In our paper, this segmentation is disbursed using K-means agglomeration algorithmic rule for higher performance. This enhances the growth boundaries more and is extremely quick compared to several alternative clustering algorithms. This paper produces the reliable results that are less sensitive to error.