Hybrid approach for brain tumor detection and classification in magnetic resonance images

Computerized methods are used in medical imaging to image the inner portions of the human body for medical diagnosis. Image segmentation plays an important role in diagnosis, surgical planning, navigation and various medical evaluations. Manual, semi-automatic and automatic methods are existing for segmentation of the region of interest. In this paper, a hybrid approach for brain tumor detection and classification through magnetic resonance images has been proposed. First phase of the proposed approach deals with image preprocessing which includes noise filtering, skull detection, etc. The second phase deals with feature extraction of MR brain images using gray level co-occurrence matrix. Third phase deals with classification of inputs into normal or abnormal using Least Squares Support Vector Machine classifier with Multilayer perceptron kernel. Final phase is the segmentation of the tumor part from the brain using fast bounding box. The experiments were carried out on 100 images consisting of 25 normal and 75 abnormal from a real human brain and synthetic MRI dataset. The classification accuracy on both training and test images was found to be 96.63%.