Efficient Detection of Brain Tumor from MRIs Using K-Means Segmentation and Normalized Histogram

Magnetic resonance imaging (MRI) is a technique which is used for the evaluation of the brain tumor in medical science. In this paper, a methodology to study and classify the image de-noising filters such as Median filter, Adaptive filter, Averaging filter, Un-sharp masking filter and Gaussian filter is used to remove the additive noises present in the MRI images i.e. Gaussian, Salt & pepper noise and speckle noise. The de-noising performance of all the considered strategies is compared using PSNR and MSE. A novel idea is proposed for successful identification of the brain tumor using normalized histogram and segmentation using K-means clustering algorithm. Efficient classification of the MRIs is done using Naive Bayes Classifier and Support Vector Machine (SVM) so as to provide accurate prediction and classification.