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.

[1]  D. Lashkari,et al.  Segmenting Glioma in Multi-Modal Images using a Generative Model for Brain Lesion Segmentation , 2012 .

[2]  Alan L. Yuille,et al.  Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification , 2008, IEEE Transactions on Medical Imaging.

[3]  Baba C. Vemuri,et al.  Nonnegative Factorization of Diffusion Tensor Images and Its Applications , 2011, IPMI.

[4]  Jacek M. Zurada,et al.  Lung segmentation based on Nonnegative Matrix Factorization , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[5]  Michael Lindenbaum,et al.  Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Nelly Gordillo,et al.  State of the art survey on MRI brain tumor segmentation. , 2013, Magnetic resonance imaging.

[7]  Michael W. Berry,et al.  Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..

[8]  Bjoern H. Menze,et al.  Multi-modal Brain Tumor Segmentation via Latent Atlases , 2012 .

[9]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[10]  Dimah Dera,et al.  Level set segmentation using non-negative matrix factorization with application to brain MRI , 2015 .

[11]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[12]  Hyunsoo Kim,et al.  Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method , 2008, SIAM J. Matrix Anal. Appl..

[13]  A. Hamamci,et al.  Multimodal Brain Tumor Segmentation Using The “Tumor-cut” Method on The BraTS Dataset , 2012 .

[14]  Dong Hye Ye,et al.  Context-sensitive Classication Forests for Segmentation of Brain Tumor Tissues , 2012 .

[15]  Ezequiel Geremia,et al.  Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images , 2011 .