Detection & Classification of Brain Tumour

The use of digital images has become a subject of widespread interest in different areas such as medical technological application and many others. There are lots of examples where image processing helps to analyze interpret and make decisions. The main use of image processing is to improve the quality of the images for human interpretation, or the perception of the machines independently. In this paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through MRI. Brain Image classification techniques are studied. Brain tumor detection in MR imaging is important in medical diagnosis because it provides information associated to anatomical structures, necessary for treatment planning and patient follow-up. In this project a brain tumor Detection and Classification System is developed. The image processing techniques such as preprocessing and feature extraction have been implemented for the detection of brain tumor in the MRI images. In this paper extraction of texture features in the detected tumor is achieved using Gray Level Co-occurrence Matrix (GLCM). SVM and K-Nearest neighbour classifier is used to classify MRI brain image into abnormal and healthy image.