Parallel genetic-based algorithm on multiple embedded graphic processing units for brain magnetic resonance imaging segmentation

Abstract Medical imaging has played an important role in helping physicians to make clinical diagnoses. Magnetic resonance imaging technology has been used to image the anatomy of the brain. Typically, image segmentation is utilized to observe the brain's anatomical structures and its changes, and to identify pathological regions. In this paper, we propose an efficient parallel fuzzy c-means clustering algorithm for segmenting images on multiple embedded graphic processing unit systems, NVIDIA TK1. The experimental results demonstrate that the maximum speedups of the proposed algorithm on 15 TK1s greater than 12 times and 7 times than that of fuzzy c-means algorithm with single ARM and Intel Xeon CPUs, respectively. These experimental results show that the proposed algorithm can significantly address the complexity and challenges of the brain magnetic resonance imaging segmentation problem.

[1]  Barbara Cutler,et al.  Robust Adaptive 3-D Segmentation of Vessel Laminae From Fluorescence Confocal Microscope Images and Parallel GPU Implementation , 2010, IEEE Transactions on Medical Imaging.

[2]  Ross T. Whitaker,et al.  GIST: an interactive, GPU-based level set segmentation tool for 3D medical images , 2004, Medical Image Anal..

[3]  Markus Hadwiger,et al.  Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets , 2009, IEEE Transactions on Visualization and Computer Graphics.

[4]  Jie-sheng Wang,et al.  Optimization of Fuzzy C-Means Clustering by Genetic Algorithms Based on Sizable Chromosome , 2009, 2009 Chinese Conference on Pattern Recognition.

[5]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[6]  Martin Rumpf,et al.  Level set segmentation in graphics hardware , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  M. Rakesh,et al.  Image Segmentation and Detection of Tumor Objects in MR Brain Images Using FUZZY C-MEANS ( FCM ) Algorithm , 2012 .

[8]  Wolfgang Strasser,et al.  Extracting regions of interest applying a local watershed transformation , 2000 .

[9]  Ewout Vansteenkiste,et al.  Spatially Coherent Fuzzy Clustering for Accurate and Noise-Robust Image Segmentation , 2013, IEEE Signal Processing Letters.

[10]  Yuan Zhang,et al.  A Parallel Image Segmentation Method Based on SOM and GPU with Application to MRI Image Processing , 2014, ISNN.

[11]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[12]  John Paul Walters,et al.  Evaluating the use of GPUs in liver image segmentation and HMMER database searches , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[13]  Anthony J. Sherbondy,et al.  Fast volume segmentation with simultaneous visualization using programmable graphics hardware , 2003, IEEE Visualization, 2003. VIS 2003..

[14]  Sanghamitra Bandyopadhyay,et al.  MRI brain image segmentation by fuzzy symmetry based genetic clustering technique , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[16]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[18]  Che-Lun Hung,et al.  Efficient Brain MRI Segmentation Algorithm on TK1 , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[19]  S Petroudi,et al.  Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. , 2015, Journal of neuroradiology. Journal de neuroradiologie.

[20]  G.B. Coleman,et al.  Image segmentation by clustering , 1979, Proceedings of the IEEE.

[21]  Wilfried Philips,et al.  MRI Segmentation of the Human Brain: Challenges, Methods, and Applications , 2015, Comput. Math. Methods Medicine.

[22]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.