An Improved C-Means Model for MRI Segmentation
Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1314550Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 441
 R. D. Nowak, "Wavelet-based Rician noise removal for magnetic resonance imaging," IEEE Transactions on Image Processing, vol. 8, pp. 1408-1419, 1999.
 N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, et al., "Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI)," IEEE transactions on biomedical engineering, vol. 51, pp. 966-970, 2004.
 M. Lysaker, A. Lundervold, and X.-C. Tai, "Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time," IEEE Transactions on image processing, vol. 12, pp. 1579-1590, 2003.
 K. Ugurbil, "Magnetic resonance imaging at ultrahigh fields," IEEE Transactions on Biomedical Engineering, vol. 61, pp. 1364-1379, 2014.
 I. Despotović, B. Goossens, and W. Philips, "MRI segmentation of the human brain: challenges, methods, and applications," Computational and mathematical methods in medicine, vol. 2015, 2015.
 R. Y. Zhong, Q. Dai, T. Qu, G. Hu, and G. Q. Huang, "RFID-enabled real-time manufacturing execution system for mass-customization production," Robotics and Computer-Integrated Manufacturing, vol. 29, pp. 283-292, 2013.
 R. Y. Zhong, G. Q. Huang, S. L. Lan, Q. Y. Dai, C. Xu, and T. Zhang, "A Big Data Approach for Logistics Trajectory Discovery from RFID-enabled Production Data," International Journal of Production Economics, vol. 165, pp. 260-272, 2015.
 M. Evertsson, P. Kjellman, M. Cinthio, S. Fredriksson, R. in't Zandt, H. Persson, et al., "Multimodal detection of iron oxide nanoparticles in rat lymph nodes using magnetomotive ultrasound imaging and magnetic resonance imaging," IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 61, pp. 1276-1283, 2014.
 E. S. H. Ibrahim, R. A. Pooley, M. D. Bridges, J. G. Cernigliaro, and W. E. Haley, "Kidney stone imaging with 3D ultra-short echo time (UTE) magnetic resonance imaging. A phantom study," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014, pp. 2356-2359.
 R. Y. Zhong, S. T. Newman, G. Q. Huang, and S. L. Lan, "Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives," Computers & Industrial Engineering, vol. 101, pp. 572-591, 2016.
 W. H. Zhu and Y. Shen, "A segmentation approach for tissue images using non-dominated sorting GA," in Anti-counterfeiting, Security, and Identification (ASID), 2016 10th IEEE International Conference on, 2016, pp. 1-5.
 T. Chitiboi and L. Axel, "Magnetic resonance imaging of myocardial strain: A review of current approaches," Journal of Magnetic Resonance Imaging, 2017.
 A. Makropoulos, I. S. Gousias, C. Ledig, P. Aljabar, A. Serag, J. V. Hajnal, et al., "Automatic whole brain MRI segmentation of the developing neonatal brain," IEEE transactions on medical imaging, vol. 33, pp. 1818-1831, 2014.
 R. Y. Zhong, S. Lan, C. Xu, Q. Dai, and G. Q. Huang, "Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing," The International Journal of Advanced Manufacturing Technology, vol. 84, pp. 5-16, April 2016.
 S. Roy, Q. He, E. Sweeney, A. Carass, D. S. Reich, J. L. Prince, et al., "Subject-specific sparse dictionary learning for atlas-based brain MRI segmentation," IEEE journal of biomedical and health informatics, vol. 19, pp. 1598-1609, 2015.
 M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, "Fuzzy c-means clustering with local information and kernel metric for image segmentation," IEEE Transactions on Image Processing, vol. 22, pp. 573-584, 2013.
 S. L. Lan, H. Zhang, R. Y. Zhong, and G. Q. Huang, "A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process," Industrial Management & Data Systems, vol. 116, pp. 1024-1042, 2016.
 E. Özceylan, M. Kabak, and M. Dağdeviren, "A fuzzy-based decision-making procedure for machine selection problem," Journal of Intelligent & Fuzzy Systems, vol. 30, pp. 1841-1856, 2016.
 M. L. Wang, R. Y. Zhong, Q. Y. Dai, and G. Q. Huang, "A MPN-based scheduling model for IoT-enabled hybrid flow shop manufacturing," Advanced Engineering Informatics, vol. 30, pp. 728-736, 2016.
 R. Y. Zhong, G. Q. Huang, S. L. Lan, Q. Y. Dai, T. Zhang, and C. Xu, "A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing," Advanced Engineering Informatics, vol. 29, pp. 799-812, 2015.
 Y. F. Zhang, G. Zhang, T. Qu, Y. Liu, and R. Y. Zhong, "Analytical target cascading for optimal configuration of cloud manufacturing services," Journal of Cleaner Production, vol. 151, pp. 330-343, 2017.
 S. Bruers, "A discussion on maximum entropy production and information theory," Journal of Physics A: Mathematical and Theoretical, vol. 40, p. 7441, 2007.
 C. H. Liu, R. Y. Zhong, Y. E. Yan, and X. Hu, "CEP-Based Massive Data Processing Approach for RFID Data," Advanced Materials Research, vol. 317, pp. 350-353, 2011.
 R. Y. Zhong, G. Q. Huang, and Q. Y. Dai, "A Big Data Cleansing Approach for n-dimensional RFID-Cuboids," Proceeding of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2014), 21-23 May, Taiwan, pp. 289-294, 2014.
 S. P. Lu, C. Xu, and R. Y. Zhong, "An Active RFID Tag-Enabled Locating Approach with Multipath Effect Elimination in AGV," IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 1333-1342, 2016.