An Effective & Automated MR Brain Image Segmentation

The image processing is an interesting and challenging field now a days and medical image processing plays a major role in it. The medical images are used to analysis the diseases like brain tumor, cancer, diabetes, etc. The brain tumor is one of the dangerous diseases where many people suffer from this disease. Image segmentation is used to take out the suspicious parts from medical images like MRI, CT scan, and Mammography etc. For MRI brain image segmentation adaptive k means clustering is used. The feature extraction is done using the GLCM (Gray Level Co-occurrence Matrix) which stay away from the creation of misclustered region. The feature selection is done to improve the classifier accuracy using PCA (Principle Component Analysis). A PSVM (Proximal Support Vector Machines) classifier is used to automatically detect the tumor from MR brain image which is faster and efficient than the existing method SVM.