Brain tumor segmentation based on non negative matrix factorization and fuzzy clustering

The problem of computational brain tumor segmentation has attracted researchers over a decade because of its high clinical relevance and challenging nature. Automatic and accurate detection of brain tumor is one of the major areas of research in medical image processing which helps radiologists for precise treatment planning. Magnetic Resonance Imaging (MRI) is one of the widely used imaging modality for visualizing and assessing the brain anatomy and its functions in non-invasive manner. In this paper a novel approach for brain tumor segmentation based on Non-Negative Matrix Factorization(NMF) and Fuzzy clustering is proposed. Proposed algorithm is tested on BRATS 2012 training database of High Grade and Low Grade Glioma tumors with clinical and synthetic data of 80 patients. Various evaluation parameters like Dice index, Jaccard index, Sensitivity, Specificity are evaluated. Comparison of experimental results with other state of the art brain tumor segmentation methods demonstrate that proposed method outperforms existing segmentation techniques.