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Comparative Analysis of Proposed FCM Clustering Integrated Enhanced Firefly-Optimized Algorithm (En-FAOFCM) for MR Image Segmentation and Performance Evaluation

Partha Ghosh, Sitansu Kumar Das, Kalyani Mali

Abstract


Image segmentation has a significant responsibility in diagnosis and treatment of diseases. To scan patients and determine the severity of certain injuries in hospitals, magnetic resonance imaging (MRI) method is normally used. This paper emphasizes on comparative study of segmentation techniques for segmenting MRI brain images. In this regard, to group the pixels of images in the intensity space, unsupervised clustering techniques are used. Here enhanced firefly algorithm is used as the fundamental optimization technique. The proposed technique is applied on several simulated and real T1-weighted for normal magnetic resonance brain images. Results of these segmentation techniques are analyzed based on four performance metrics; UnS, OvS, InCS and Tanimoto coefficient. Also, the performance of the proposed enhanced firefly algorithm optimized fuzzy image segmentation approach has been evaluated by comparing it with some state-of-the-art segmentation algorithms. The accuracy has been evaluated by comparing the results with the ground truth of each processed image.

Cite this Article
Partha Ghosh, Sitansu Kumar Das, Kalyani Mali. Comparative Analysis of Proposed FCM Clustering Integrated Enhanced Firefly-Optimized Algorithm (En-FAOFCM) for MR Image Segmentation and Performance Evaluation. Journal of Image Processing & Pattern Recognition Progress. 2016; 3(1): 32–44p.


Keywords


FCM, FAFCM, UnS, OvS, InCS, Tanimoto coefficient

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References


Fayyad UM, Piatetsky-Shapiro G, Smyth P, et al. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press; 1996.

Murty MN, Jain AK, Flynn PJ. Data Clustering: A Review. ACM Comput Surv. 1999; 31(3): 264–323p.

Jain AK, Dubes RC. Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice Hall; 1988.

Krishna Kant Singh, Akansha Singh. A Study of Image Segmentation Algorithms for Different Types of Images. IJCSI International Journal of Computer Science Issues. 2010.

Fu SK, Mui JK. A Survey on Image Segmentation. Pattern Recogn. 1981; 13: 3–16p.

Acharya J, Gadhiya S, Raviya. Segmentation Techniques for Image Analysis: A Review. Int J Comput Sci Manage Res. 2013; 2(4): 1218–21p.

Naik D, Shah P. A Review on Image Segmentation Clustering Algorithms. Int J Comput Sci Inform Technol. 2014; 5(3): 3289–93p.

Christe SA, Malathy K, Kandaswamy A. Improved Hybrid Segmentation of Brain MRI Tissue and Tumor Using Statistical Features. ICTACT J Image Video Process. 2010; 1(1): 34–49p.

Seerha GK, Kaur R. Review on Recent Image Segmentation Techniques. Int J Comp Sci Eng (IJCSE). 2013; 5(2): 109–12p.

Tan SK, Lim HW, Isa MN. Novel Initialization Scheme for Fuzzy C-Means Algorithm on Color Image Segmentation. Appl Soft Comput. 2013; 13: 1832–1852p.

Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press; 1981.

Bezdek JC, Hall LO, Clarke LP. Review of MR Image Segmentation Techniques Using Pattern Recognition. Med Phys. 1993; 20: 1033–1048p.

Alia OM, Mandava R, Ramachandram D, et al. Harmony Search Based Cluster Initialization for Fuzzy C-Means Segmentation of MR Images. In Int Tech Conference of IEEE Region 10 (TENCON), Singapore. 2009; 1–6p.

Alia OM, Mandava R, Ramachandram D, et al. A Novel Image Segmentation Algorithm Based on Harmony Fuzzy Search Algorithm. In Int Conf Soft Computing and Pattern Recognition, SOCPAR ’09, Malacca, Malaysia. 2009; 335–340p.

Bezdek JC, Hathaway RJ. Optimization of Fuzzy Clustering Criteria Using Genetic Algorithms. In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 2, Orlando, FL, USA. 1994; 589–594p.

Maulik U, Saha I. Modified Differential Evolution Based Fuzzy Clustering for Pixel Classification in Remote Sensing Imagery. Pattern Recogn. 2009; 42(9): 2135–2149p.

Kanade PM, Hall LO. Fuzzy Ants and Clustering. IEEE Trans Syst, Man Cybern. Part A. 2007; 37(5): 758–769p.

Das S, Abraham A, Konar A. Metaheuristic Pattern Clustering: An Overview. In Metaheuristic Clustering, Springer. 2009; 1–62p.

Das S, Konar A. Automatic Image Pixel Clustering with an Improved Differential Evolution. Appl Soft Comput. 2009; 9(1): 226–236p.

Saha S, Bandyopadhyay S. A New Point Symmetry Based Fuzzy Genetic Clustering Technique for Automatic Evolution of Clusters. Information Sciences. 2009; 179(19): 3230–3246p.

Saha S, Bandyopadhyay S. MRI Brain Image Segmentation by Fuzzy Symmetry Based Genetic Clustering Technique. In IEEE Congress on Evolutionary Computation, CEC. 2007; 4417–4424p.

Campello R, Hruschka E, Alves V. On the Efficiency of Evolutionary Fuzzy Clustering. J Heuristics. 2009; 15(1): 43–75p.

Pakhira MK, Bandyopadhyay S, Maulik U. A Study of Some Fuzzy Cluster Validity Indices, Genetic Clustering and Application to Pixel Classification. Fuzzy Set Syst. 2005; 155(2): 191–214p.

Maulik U, Bandyopadhyay S. Fuzzy Partitioning Using a Real Coded Variable-Length Genetic Algorithm for Pixel Classification. IEEE Trans Geosci Remote Sens. 2003; 41(5): 1075–1081p.

Mayer A, Greenspan H. An Adaptive Mean-Shift Framework for MRI Brain Segmentation. IEEE Trans Med Imag. 2009; 28(8): 1238–1249p.

Azian Azamimi Abdullah, Bu Sze Chize, Yoshifumi Nishio. Implementation of an Improved Cellular Neural Network Algorithm for Brain Tumor Detection. Int Conf on Biomedical Engg, (ICoBE), Penang. Feb 2012; 27–28p.

Mustaqeem A, Ali Javed, Fatima T. An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresh Holding Based Segmentation. Int Journal of Image, Graphics and Signal Processing (IJIGSP). 2012; 10: 34–39p.

Vijay J, Subhashini J. An Efficient Brain Tumor Detection Methodology Using K-Means Clustering Algorithm. IEEE IntConf on Communication and Signal Processing. Apr 3–5, 2013; 653–657p.

Bandhyopadhyay SK, Paul TU. Automatic Segmentation of Brain Tumor from Multiple Images of Brain MRI. Int J Appl Innovat Eng Manage (IJAIEM). 2013; 2(1): 240–8p.

Roy S, Bandyopadhyay SK. Detection and Quantification of Brain Tumor from MRI of Brain and its Symmetric Analysis. Int Journal of Info and Comm Tech Research, KY, USA. Jun 2012.

Padole, Chaudhari. Detection of Brain Tumor in MRI Images Using Mean Shift Algorithm and Normalized Cut Method. Int J Engg Adv Tech. Jun 2012.

Xue J-H, Pizurica A, Philips W, et al. An Integrated Method of Adaptive Enhancement for Unsupervised Segmentation of MRI Brain Images. Pattern Recogn Lett. 2003; 24(15): 2549–2560p.

Tu Z, Narr KL, Dollar P, et al. Brain Anatomical Structure Segmentation by Hybrid Generative Models. IEEE Trans Med Imag. 2008; 495–508p.

Gui L, Lisowski R, Faundez T, et al. Morphology-Driven Automatic Segmentation of MR Images of the Neonatal Brain. Med Image Anal. 2012; 16(8): 1565–1579p.

Ortiz A, Gorriz JM, Ramirez J, et al. Improving MR Brain Image Segmentation Using Self-Organising Maps and Entropy-Gradient Clustering. Information Sciences. 2014; 117–136p.

Christe SA, Malathy K, Kandaswamy A. Improved Hybrid Segmentation of Brain MRI Tissue and Tumor Using Statistical Features. ICTACT J Image Video Process. 2010; 1(1): 34–49p.

Kapur T, Eric W, Grimson L, et al. Segmentation of Brain Tissue from Magnetic Resonance Images. Med Image Anal. 1996; 1(2): 109–127p.

Masutani Y, Schiemann T, Hohne KH. Medical Image Computing and Computer-Assisted Intervention. MICCAI’98: Proceedings of the 1st International Conference, Cambridge, MA, USA. Oct

–13, 1998; 1496. Berlin, Germany: Springer; 1998.

Warfield SK, Kaus M, Jolesz FA, et al. Adaptive, Template Moderated, Spatially Varying Statistical Classification. Med Image Anal. 2000; 4(1): 43–55p.

Yang X-S. Firefly Algorithm, Stochastic Test Functions and Design Optimisation. Int J Bio-Inspir Com. 2010; 2: 78–84p.

Fister I, Fister Jr I, Yang X-S, et al. A Comprehensive Review of Firefly Algorithms. Swarm Evol Comput. 2013; 13: 34–46p.

Yang XS. Nature-Inspired Metaheuristic Algorithms. UK: Luniver Press; 2008; 128p. ISBN-13: 978-1905986101.

Alsmadi Mutasem K. A Hybrid Firefly Algorithm with Fuzzy-C Mean Algorithm for MRI Brain Segmentation. Am J Appl Sci. 2014; 11(9): 1676–1691p.

Alomoush WK, Huda SN, Abdullah S, et al. Segmentation of MRI Brain Images using FCM Improved by Firefly Algo. J Appl. Sc. 2013. ISSN: 1812-5654.

Tilahun SL, Ong HC. Modified Firefly Algorithm. J Appl Math. 2012b; 1: 1–12p.

BrainWeb [Online]. www.bic.mni.mcgill.ca/brainweb/

IBSR [Online]. http://www.cma.mgh.harvard.edu/ibsr/

Duda RO, et al. Pattern Classification. John Wiley & Sons; 2012.

Shen S, Sandham W, Granat M, et al. MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization. IEEE Trans Inf Technol Biomed. Sep 2005; 9(3).


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