Brain Tumor Segmentation using Multiobjective Fuzzy Clustering

  • Olfa Mohamed Limam Institut superieur d'informatique, University of Manar
Keywords: Brain tumor image segmentation, fuzzy clustering, multiobjective optimization, genetic algorithm.

Abstract

The segmentation of magnetic resonance images plays a crucial role in medical image analysis because it extracts the required area from the image. Despite intensive research, it still remains a challenging problem and there is a need to develop an appropriate and efficient medical image segmentation method. In this paper, we propose a clustering approach for brain tumor segmentation to diagnose accurately the region of cancer. Applied to magnetic resonance image brain our method provides better identification of brain tumor.

References

(1) Shen Shan, Sandham William, Granat Malcolm, and Sterr Annette. Mri fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Transactions on Information Technology in Biomedicine, 9(3):459–467, 2005.

(2) P.Tamije Selvy, V. Palanisamy, and T. Purusothaman. Performance analysis of clustering algorithms in brain tumor detection of mr images. European Journal of Scientific Research, 62(3):321–330, 2011.

(3) Suchita Yadav and Meshram Sachin. Brain tumor detection using clustering method. International Journal of Computational Engineering Research, 3(4):11–14, 2013.

(4) Miin-Shen Yang, Yu-Jen Hu, Karen Chia-Ren, and Charles Chia-Lee Lin. Segmentation techniques for tissue differentiation in mri of ophthalmology using fuzzy clustering algorithms. Magentic resonance Imaging, 20:173–179, 2002.

(5) Sanghamitra Bandyopadhyay, Anirban Mukhopadyay, and Ujjwal. Maulik. Combining multiobjective fuzzy clustering and probabilistic ann classifier for unsupervised pattern classification: Application to satellite image segmentation. IEEE on Evolutionary Computation, pages 877–883, 2008.

(6) 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):164–171, 2007.

(7) Chunlin Li, DB Goldgof, and LO Hall. Knowledge-based classification and tissue labeling of mr images of human brain. IEEE transactions on Medical Imaging, 12(4):740–750, 2002.

(8) Eman A. Abdel Maksoud, Mohammed Elmogy, and Rashid Mokhtar Al-Awadi. MRI brain tumor segmentation system based on hybrid clustering techniques. In Advanced Machine Learning Technologies and Applications - Second International Conference, AMLTA, pages 401–412, 2014.

(9) L. Dzung Pham and Jerry L. Prince. Knowledge-based classification and tissue labeling of mr images of human brain. IEEE transactions on Medical Imaging, 18(9):737–752, 1999.

(10) Jayram K. Udupa and K. Saha Punam. Fuzzy connectedness and image segmentation. Proceedings of the IEEE, 91:1649–1669, 2003.

(11) R Sivakumarl Neeraja R Menon, M Karnan. Brain tumor segmentation in mri image using unsupervised artificial bee colony and fcm clustering. International Journal of Computer Science and Management Research, 2:2450–2454, 2013.

(12) Mutasem K. Alsmadi. Mri brain segmentation using a hybrid artificial bee colony algorithmwith fuzzy-c mean algorithm. Journal of Applied Sciences, 15:100–109, 2015.

(13) Zhanshen Feng and Boping Zhang. Fuzzy clustering image segmentation based on particle swarm optimization. Telecommunication Computing Electronics and Control, 13(1):128–136, 2015.

(14) P. Natarajan, N. Krishnan, N.S. Kenkre, S. Nancy, and B.P. Singh. Tumor detection using threshold operation in mri brain images. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–4, 2012.

(15) K. Thapaliya, , and Kwon Goo-Rak. Extraction of brain tumor based on morphological operations. 8th International Conference on Computing Technology and Information Management (ICCM), 20:515 – 520, 2012.

(16) Anam Mustaqeem, Ali Javed, and Tehseen Fatima. An efficient brain tumor detection algorithm using watershed thresholding based segmentation. Interntional Journal of Image, Graphics and Signal Processing, 10:34–39, 2012.

(17) Tomasz Weglinski and Anna Fabijanska. Brain tumor segmentation from mri data sets using region growing approach. Proceedings of VIIth International Conference on Perspective Technologies and Methods in MEMS Design, pages 185 – 188, 2011.

(18) Won Chulho and Kim Dong-Hun. Region growing method using edge sharpness for brain ventricle detection. SICE, Annual Conference, pages 1930–1933, 2007.

(19) K.S.A Viji and J. JayaKumari. Modified texture based region growing segmentation of mr brain images. IEEE Conference on Information Communication Technologies (ICT), pages 691–695, 2013.

(20) Peter Gibbs, David L. Buckley, Stephen J Blackband, and Anthony Horsman. Tumour volume determination from mr images by morphological segmentation. Physics in Medicine and Biology, 41:2437–2446, 1996.

(21) Frederic Lachmann and Christian Barillotr. Brain tissue classification from mri data by means of texture analysis. Medical Imaging VI: Image Processing, 72, 1992.

(22) D.M. Joshi, N. K. Rana, and V.M. Misra. Classification of brain cancer using artificial neural network. International Conference on Electronic Computer Technology (ICECT), pages 112–116.

(23) L.O Hall, A.M. Bensaid, L.P. Clarke, R.P. Velthuizen, and M.S. Silbige rand James C. Bezdek. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Network, 3(5):672–682, 1992.

(24) W. M. Wells, W. L. Grimson, R. Kikinis, and F. A . Jolesz. Adaptive segmentation of mri data. IEEE Transactions on Medical Imaging, 15(4):429–442, 1996.

(25) K. V. Leemput, F. Maes, D. Vandermeulen, and P. Suetens. Automated model-based tissue classification of mr images of the brain. IEEE Transactions on Medical Imaging, 18(10):897–908.

(26) Sudipta Acharya, Yamini Thadisina, and Sriparna Saha. Multi-objective clustering of tissue samples for cancer diagnosis. International Conference on Advances in Computing, Communications and Informatics, pages 1059–1064, 2014.

(27) Olfa Limam and Fouad Ben Abdelaziz. Multicriteria fuzzy clustering for brain image segmentation. 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), pages 1019 – 1031, 2013.

(28) Bong Chin-Wei and Mandava Rajeswari. Multiobjective optimization approaches in image segmentation - the directions and challenges. International Journal of Advances in Soft Computing and Its Applications, 2(2):41–64, 2010.

(29) Anil Kumar Jain, M Narasimha Murty, and Patrick Joseph Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):265–323, 1999.

(30) Marcel Prastawa, Elizabeth Bullitt, Sean Ho, and Guido Gerig. A brain tumor segmentation framework based on outlier detection. Medical Image Analysis, 8(3):275–283, 2004.

(31) Xiang-Yang Wang and Juan Bu. A fast and robust image segmentation using fcm with spatial information. Digital Signal Processing, 20:1173–1182, 2010.

(32) Jie Liu, Jigui Sun, and Shengsheng Wang. Pattern recognition: An overview. International Journal of Computer Science and Network Security IJCSNS, 6(6):57–60, 2006.

(33) Brainweb: Simulated brain database, 2011, available at http://www.bic.mni.mcgill.ca/brainweb.

(34) K.Y. Yeung and Walter L. Ruzzo. An empirical study of principal

component analysis for clustering gene expression data. 2001.

(35) Maulik Ujjwal and Mukhopadhyay Anirban. A multiobjective approach to mr brain image. Applied Soft Computing 11, 872–880.

Published
2016-03-03
How to Cite
Limam, O. M. (2016). Brain Tumor Segmentation using Multiobjective Fuzzy Clustering. Transactions on Machine Learning and Artificial Intelligence, 4(1), 58. https://doi.org/10.14738/tmlai.41.1840