Evaluating an Evolutionary Particle Swarm Optimization for Fast Fuzzy C-Means Clustering on Liver CT Images

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept of fractional calculus is used to control the convergence rate of particles, wherein each one of them represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data. The experimental results based on the Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed algorithm is fast, accurate, and less time consuming. Abder-Rahman Ali Scientific Research Group in Egypt (SRGE), Egypt Micael S. Couceiro University of Coimbra, Portugal & Ingeniarius, Lda., Mealhada, Portugal Ahmed M. Anter Scientific Research Group in Egypt (SRGE), Egypt & Mansoura University, Egypt Aboul Ella Hassanian Scientific Research Group in Egypt (SRGE), Egypt & Cairo University, Egypt

[1]  P M Silverman,et al.  Routine helical CT of the abdomen: image quality considerations. , 1993, Radiology.

[2]  Yuehui Chen,et al.  Computational Intelligence in Bioinformatics , 2008, Computational Intelligence in Bioinformatics.

[3]  R. Krishnamoorthi,et al.  Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model , 2011, Int. J. Comput. Vis. Image Process..

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  Aboul Ella Hassanien,et al.  Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[6]  D A Bluemke,et al.  Spiral CT of the liver. , 1993, AJR. American journal of roentgenology.

[7]  P M Silverman,et al.  Helical (spiral) CT of the abdomen. , 1993, AJR. American journal of roentgenology.

[8]  P W Ostalczyk.  A note on the Grünwald–Letnikov fractional-order backward-difference , 2009 .

[9]  Abdul Rahman Ramli,et al.  Survey on liver CT image segmentation methods , 2011, Artificial Intelligence Review.

[10]  Zhou Xian-cheng.  Image Segmentation Based on Modified Particle Swarm Optimization and Fuzzy C-Means Clustering , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[11]  Jon Atli Benediktsson,et al.  An efficient method for segmentation of images based on fractional calculus and natural selection , 2012, Expert Syst. Appl..

[12]  Raghuveer M. Rao,et al.  Darwinian Particle Swarm Optimization , 2005, IICAI.

[13]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[14]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[15]  E. Sreenivasa Reddy,et al.  Tizhoosh ’ s Fuzzy membership function To measure the image fuzziness , 2012 .

[16]  Bing-yuan Cao,et al.  Fuzzy Information and Engineering 2010 - Volume I, Proceedings of the 5th Annual Conference on Fuzzy Information and Engineering, ACFIE 2010, Sepember 23-27, 2010, Huludao, China , 2010, ACFIE.

[17]  D A Bluemke,et al.  Spiral CT of the liver: current applications. , 1994, Seminars in ultrasound, CT, and MR.

[18]  L Solbiati,et al.  Guidelines for the use of contrast agents in ultrasound. January 2004. , 2004, Ultraschall in der Medizin.

[19]  Witold Pedrycz,et al.  Advances in Fuzzy Clustering and its Applications , 2007 .

[20]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[21]  Ajith Abraham,et al.  Fuzzy C-means and fuzzy swarm for fuzzy clustering problem , 2011, Expert Syst. Appl..

[22]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .