K-MEANS, WATERSHED AND TEXTURE-BASED IMAGE SEGMENTATION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Nowadays image processing is a highly challenging field in detecting the brain tumor from MRI Brain Scan. It has become a popular in our research area. In this paper, MRI brain image is used to detect the brain tumor. This system includes conversion to gray, test the brain, whether it is benign or malignant, Threshold Segmentation is the simplest segmentation method, canny operator for edge detection and morphological Based de-noising used for removing the noise, clustering method combination of MRF and CRF, Watershed segmentation is the best method for grouping pixels of an image on the basis of intensity, by combining k-means with watershed Segmentation overcome the Over Segmentation and use texture Segmentation. The detailed procedure is implemented in MATLAB. By this, the brain tumor is detected accurately and also it is determined whether the patients can be cured by medicine or not.

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Keywords

K-means, watershed and Texture Segmentation, Canny Operator, Morphological Based denoising, MRF AND CRF.