Metaheuristic Optimization Design for Image Segmentation: Applications to Brain MRI Images

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-54 Number-1
Year of Publication : 2017
Authors : Sara Riahi, Azzeddine Riahi
  10.14445/22312803/IJCTT-V54P109

MLA

Sara Riahi, Azzeddine Riahi "Metaheuristic Optimization Design for Image Segmentation: Applications to Brain MRI Images". International Journal of Computer Trends and Technology (IJCTT) V54(1):40-55, December 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
This paper presents a new approach for image segmentation based on the metaheuristic "Optimization by Ant Colonies". It is a method of classification without initial partition required or information a priori. It is based on the principle of stochastic exploration of a combined ant colony with the theory of Markov fields for modeling the field labels, and field observations. We propose to use the metaheuristic ant colonies to estimate the fields of labels and build an optimal partition of the image.

References
[1] Azzeddine Riahi ,‟‟MRI Image Segmentation by KMeans Clustering Method and Detection of Lesions‟‟, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064, Volume 4 Issue 6, Page No(2484-2492),June 2015
[2] Azzeddine Riahi, Image Segmentation Techniques Based on Fuzzy C-Means and Otsu, Applied to the Brain MRI in Tumor Detection, International Journal of Computer Sciences and Engineering,Volume-03, Issue-12 E-ISSN: 2347-2693,2015.
[3] Ian T. Young ,Jan J. Gerbrands ,Lucas J. van Vliet,‟ Fundamentals of Image Processing‟, Version 2.3 © 1995-2007 I.T. Young, J.J. Gerbrands and L.J. van Vliet
[4] A.-S. Capelle, O. Colot et C. Fernandez-Maloigne, 3D Segmentation of MR Brain Images into White Matter, Gray Matter and Cerebro-spinal Fluid by Means of Evidence Theory, Revue Lecture Notes of Computer Science Series, “Artificial Intelligence in Medicine”, pages 112–116, M. Dojat, E. Keravnou, P. Barahona (Eds.), Springer-Verlag, 2003.
[5] Siti Noraini Sulaiman, Noreliani Awang Non, Iza Sazanita Isa, Norhazimi Hamzah ,‟ Segmentation of Brain MRI Image Based on Clustering Algorithm‟, Recent Advances in Electrical and Computer Engineering, ISBN: 978-1-61804-228-6
[6] S. Bileschi and L. Wolf. A unified system for object detection, texture recognition, and context analysis based on the standard model feature set. In BMVC, 2005.
[7] Subhagata Chattopadhyay, Dilip Kumar Pratihar, Sanjib Chandra De Sarkar‟‟ A COMPARATIVE STUDY OF FUZZY C-MEANS ALGORITHM AND ENTROPY-BASED FUZZY CLUSTERING ALGORITHMS‟‟, Computing and Informatics, Vol. 30, 2011, 701–720, Page No (701-720) ,2012.
[8] El Dor, A., Clerc, M., Siarry, P.: A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization. Computational Optimization and Applications 53(1), 271–295 (2012).
[9] Ahmed Saad, Ben Smith, Ghassan Hamameh, Torsten Moller, "Simultaneous Segmentation Kinetic Parameter Estimation and Uncertainty Visualization of Dynamic PET Images", Springer on Medical Image Computing and Computer Assisted Intervention„ (MICCAI), vol. 4792, no. 2007, pp. 726-733, October 2007.
[10] Pitas, I.: Digital Image Processing Algorithms and Applications. John Wiley & Sons (2000)
[11]E Suganya, Archana ,Dr. S.Sountharrajan, Dr.C.Rajan, METAHEURISTIC OPTIMIZATION TECHNIQUE FOR FEATURE SELECTION TO DETECT THE ALZHEIMER DISEASE FROM MRI ,Journal of advanced research in dynamical and control systems ,ISSN : 1943 – 023X ,2017.
[12] Dana Cobzas, Neil Birkbeck, Mark Schmidt,Martin Jagersand,:―3DVariational Brain Tumour Segmentation Using a High Dimensional Feature Set,‖. IEEE 11th International Conference on Computer Vision, ICCV-2007, 1-8, Brazil October 2007.
[13] Bricq.S, Collet.Ch, Armspach.J.P,:‖Unifying Framework for Multimodal brain MRI Segmentation based on Hidden Markov Chains‖, Elsevier on Medical Image Analysis,March,2008. Volume 12, Issue 6, Pages 639-652, December 2008.
[14] A. Kharrat, K. Gasmi, M. Ben Messaoud, N. Benamrane and M. Ab id, “Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique”, International Journal of Software Science and Computational Intelligence (IJSSCI), 3(2), pp. 19-33, 2011. MRI Brain Tumor Classification using Support Vector Machines and Meta-Heuristic Method (PDF Download Available).
[15] Dr.M.Karnan, N.NandhaGopal, “Hybrid Marko Random Field with with Parallel Ant Colony Optimization And Fuzzy C means For MRI Brain Image Segmentation”,IEEE 2010,978-1-4244-5967-4/10.
[16] N. Lu, J. Jin, E. Michielssen, and R. L. Magin, Optimization of RF Coil Design Using Genetic HADLEY, FURSE, PARKER: RF COIL DESIGN FOR MRI USING A GENETIC ALGORITHM 285 Algorithm and Simulated Annealing Method, Nice, p. 1002, 1995.
[17] P. SANTAGO and H. D. GAGE. Quantification of MR brain images by mixture density and partial volume modeling. IEEE Trans Med Imag, 12(3):566 – 574, September 1993.
[18] S. SMITH. Fast robust automated brain extraction. Human Brain Mapping, 17:143 – 155, 2002.
[19] J. Bezdek, L. Hall, L. Clarke, "Review of MR image segmentation techniques using pattern recognition", Medical Physics, vol. 20, no. 4, pp. 1033-1048, 1993.
[20] Pham DL, Xu CY, Prince JL: A survey of current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2 (2000) 315-37 [Technical report version, JHU/ECE 99-01, Johns Hopkins University].
[21] L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, L. O. Hall, R. W. Thatcher, M. L. Silbiger, "MRI segmentation: methods and applications", Magn. Reson. Imag., vol. 13, no. 3, pp. 343-368, 1995.
[22] R. Kikinis, F. A. Jolesz, W. E. Lorensen, H. E. Cline, P. E. Stieg, P. McL. Black, "3-D reconstruction of skull base tumors from MRI data for neurosurgical planning", Proc. Soc. Mag. Reson. Med. Conf., 1991.
[23] Z.-X. Ji, Q.-S. Sun, and D.-S. Xia, “A framework with modified fast FCM for brain MR images segmentation,” Pattern Recognit., Vol. 44, no.12, pp. 999–1013, Dec. 2011.
[24] Balafar MA, Ramli AR, Saripan MI, Mashohor S, Mahmud R (2010) Improved fast fuzzy C-mean and its International Journal of Computer Trends and Technology (IJCTT) – Volume54 Issue 1- December 2017 ISSN: 2231-2803 http://www.ijcttjournal.org Page 56 application in medical image segmentation. J Circ Syst Comput 19: 203–214.
[25] A. Karimian, S. Yazdani, and M. Askari, “Reducing the absorbed dose in analogue radiography of infant chest images by improving the image quality, using image processing techniques,” Radiat. Prot Dosim., Vol. 147, no. 1–2, pp. 176–9, Jul. 2011.
[26] M. Sezgin et al., "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic imaging, vol. 13, no. 1, pp. 146-168, 2004.
[27] Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013).
[28] Vishwakarma, B., Yerpude, A.: A meta-heuristic approach for image segmentation using firefly algorithm. Int. J. Comput. Trends Technol. (IJCTT) 11(2), 69–73 (2014).
[29] Yin, P.Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007).
[30] Bandyopadhyay S, Saha S, Maulik U, Deb K ,A simulated annealing based multi-objective optimization algorithm: AMOSA. IEEE Trans Evol Comput 12(3):269–283,2008.
[31] Weibei Dou, Su Ruan, Daniel Bloyet, and Jean-Marc Constans. A framework of fuzzy information fusion for the segmentation of brain tumor tissues on mr images. Image and Vision Computing, 25(2), 2007.
[32] Handl, J., and Knowles, J.," On semi-supervised clustering via multiobjective optimization", 8th annual conference on Genetic and evolutionary computation (GECCO‟2006), ACM Press (2006a), 1465–1472.
[33]Puzicha, J., Hofmann, T. and Buhmann, J. M.," Histogram Clustering for Unsupervised Image Segmentation". Computer Vision and Pattern Recognition, Vol. 2. IEEE press, (2000), 602-608.
[34] ORTIZ A, GORRIZ J M, RAMIREZ J, et al. Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering . Information Sciences, 2014, 262(3):117-136.
[35] riparna Saha , Sanghamitra Bandyopadhyay, MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique, Fundamenta Informaticae, v.97 n.1-2, p.199-214, January 2009.
[36] Luque JM, Santana-Quintero LV, Hernández-Díaz AG, Coello CAC, Caballero R (2009) g-dominance: Reference point based dominance for multiobjective metaheuristics. Eur J Oper Res 197(2):685-692.
[37] C.R. Raquel and P.C. Naval, "An effective use of crowding distance in multiobjective particle swarm optimization", in Proceedings of the 2005 Genetic and Evolutionary Computation Conference, 2005, pp. 257-264.
[38] Bong, C.W., Rajeswari, M.: Multi-objective nature-inspired clustering and classification techniques for image segmentation. Appl. Soft Comput. 11(4), 3271–3282 (2011).
[39] E. D'Agostino, F. Maes, D. Vandermeulen, and P. Suetens, “A viscous fluid model for multimodal non-rigid image registration using mutual information,” in Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI '02), pp. 541–548, 2002.
[40] M. Battaglini, S. M. Smith, S. Brogi, and N. de Stefano, “Enhanced brain extraction improves the accuracy of brain atrophy estimation,” NeuroImage, vol. 40, no. 2, pp. 583–589, 2008.
[41] C. Sagiv, N. A. Sochen, and Y. Y. Zeevi, “Integrated active contours for texture segmentation,” IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1633–1646, 2006.
[42]Saranu Kavya Pooja, Koganti Koundinya, J Hemamalini, “Efficiency Measurement of Detecting Object From Video”, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Number 5 pp. 12165-12175,2015.

Keywords
Segmentation, Classification, Optimization, Ant Colony, Pareto, Metaheuristics.