Novel Approach for Brain Tumor Segmentation with Non Negative Matrix Factorization

Over a decade, automatic segmentation of brain tumor in Magnetic Resonance Imaging (MRI) is a challenging task for researchers. Large amount of data is produced by MRI and the task of marking the tumor slice by slice is a very tedious and time consuming process for radiologists and hence accurate and reliable segmentation methods are gaining more attention 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.