Automated Categorization of Multi-Class Brain Abnormalities Using Decomposition Techniques With MRI Images: A Comparative Study

Medical imaging and analysis are useful to visualize anatomic structure. However, analysis of the pathologic substrate is dif�cult and inef�cient when using simple imaging tools. The manual detection and classi�cation of brain abnormality is particularly tedious. Moreover, the currently used methodology suffers from interobserver variability during image interpretation. Magnetic resonance imaging (MRI) is an ef�cient imaging technique for revealing complex anatomical architecture, and it is highly ef�cacious for precise brain imaging. Herein, we describe a novel computer aided diagnosis method for automated processing of brain MRI images. The performances of two decomposition techniques, namely, bidimensional empirical mode decomposition and variational mode decomposition (VMD), are compared. Thereafter, bispectral feature extraction and supervised neighborhood projection embedding are implemented to represent each feature in a new subspace, for the automated classi�cation of various categories of disease. A support vector machine classi�er is used to train and test the performance accuracy. The level of classi�cation accuracy of 90.68%, 99.43% sensitivity and 87.95% speci�city is obtained using the VMDtechnique. Hence, the developed system can be used as an adjunct tool by radiologists to con�rm their screening