Lobe asymmetry-based automatic classification of brain magnetic resonance images

An automated processing system of brain magnetic resonance (MR) images is presented with application to normal versus glioma diagnosis. It exploits lobe asymmetry to distinguish the normal and abnormal brain MR images. Each MR image is first processed to emphasize edges before splitting it into right lobe and left lobe components. These are transformed into one-dimensional signals and the corresponding power spectral density functions (PSDF) are estimated. Then, a four-dimensional feature vector is formed with the energy of each PSDF and their correlation coefficient calculated by two approaches. Using leave-one-out cross validation on a dataset of seven normal and seven glioma affected MR images, 100% classification accuracy was achieved by a support vector machine classifier, with near real-time processing time.