An improved brain tumor detection and classification mechanism

Detection of brain tumor is an intricate job that has yet to be well furnished. At present, brain tumor is rising by a large scale amidst us. To detect and segment tumor from the MRI images we have designed an automated scheme. Our scheme comprises of Otsu binarization followed by K means clustering for segmentation. To extract features and reduce the dimensions of features Discrete Wavelet Transform (DWT) followed by Principle Component Analysis (PCA) is respectively used. The reduced features were submitted to a Support Vector Machine (SVM) for classification. The performance evaluation of the proposed scheme is made by comparing the results with the ground truth of each processed image using Sensitivity, Specificity and Accuracy calculations.