Improvement of CSF, WM and GM Tissue Segmentation by Hybrid Fuzzy — Possibilistic Clustering Model based on Genetic Optimization Case Study on Brain Tissues of Patients with Alzheimer’s Disease
Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. However FCM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper...
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| Published in | The International journal of networked and distributed computing (Online) Vol. 6; no. 2; pp. 63 - 77 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.04.2018
Springer |
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| Online Access | Get full text |
| ISSN | 2211-7938 2211-7946 2211-7946 |
| DOI | 10.2991/ijndc.2018.6.2.2 |
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| Abstract | Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. However FCM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(CSF) tissues using hybrid clustering process which based on: (1) FCM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) Possibilistic C-Means (PCM) algorithm for volumetric measurements of WM, GM, and CSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer’s disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FCM and PCM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high. |
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| AbstractList | Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. However FCM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(CSF) tissues using hybrid clustering process which based on: (1) FCM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) Possibilistic C-Means (PCM) algorithm for volumetric measurements of WM, GM, and CSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer’s disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FCM and PCM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high. |
| Author | Boukadoum, Mounir Lazli, Lilia |
| Author_xml | – sequence: 1 givenname: Lilia surname: Lazli fullname: Lazli, Lilia email: lilia.lazli.1@ens.etsmtl.ca organization: CoFaMic Research Centre, Department of Computer Science, UQÀM, University of Quebec, Department of Electrical Engineering, ÉTS, University of Quebec – sequence: 2 givenname: Mounir surname: Boukadoum fullname: Boukadoum, Mounir organization: CoFaMic Research Centre, Department of Computer Science, UQÀM, University of Quebec |
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| Keywords | Fuzzy c-means algorithm Alzheimer’s disease Hybrid reasoning Possibilistic c-means algorithm Brain tissue clustering Genetic algorithms |
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| References | BezdekJ-CHallL-OClarkMGoldofDClarkeL-PMedical image analysis with fuzzy modelsStat. Methods Med. Res19976191214 GroverNA study of various fuzzy clustering algorithmsInternational Journal of Engineering Research201433177181 Bala-GanesanTSukaneshRSegmentation of Brain MR Images using Fuzzy Clustering Method with Sillhouette MethodJournal on Engineering and Applied Sciences2008310792795 K. Xiao, S. Hock Ho and A. Bargiela, Automatic Brain MRI Segmentation Scheme Based on Feature Weighting Factors Selection on Fuzzy C-Means Clustering Algorithms with Gaussian Smoothing, International Journal of Computer Intelligence in Bioinformatics and Systems Biology, (2010), vol. 1(3). M. Shasidhar, V. Sudheer Raja and B. Vijay Kumar, MRI Brain Image Segmentation Using Modified Fuzzy C-Means Clustering Algorithm, Communication Systems and Network Technologies (CSNT), Int. Conference on. (2011), pp. 473–478. doi: https://doi.org/10.1109/CSNT.2011.102 D. Kang, S. Y. Shin, C. O. Sung, J. 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Arbel, Bayesian MS lesion classification modeling regional and local spatial information, in The 18th International Conference on Pattern Recognition (ICPR), Hong Kong, IEEE, (2006), pp. 984–7. HaralickR-MShapiroL-GSurvey: image segmentation techniquesComputer Vision, Graphics, and Image Processing198529100132 CaiWChenSZhangDFast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentationPattern Recognition200740825838 CaldairouBNicolasPPiotrHColinSFrancoisRA non-local fuzzy segmentation method: application to brain MRIPattern Recognition20114419161927 S. Shen, W. Sandham, M. Granat, and A. Sterr, MRI Fuzzy Segmentation of Brain Tissue using Neighborhood Attraction with Neural-Network Optimization, IEEE Transaction on Information Technology in Biomedicine, (2005), vol. 9(3). Ferreira da SilvaA-RBayesian mixture models of variable dimension for image segmentationComput Methods Programs Biomed200994114 D-E. Goldberg, Algorithmes génétiques : Exploration, optimisation et apprentissage automatique, Addison Wesley, (1997). BarraVBoireJ-YTissue Segmentation on MR Images of the Brain by Possibilistic Clustering on a 3D Wavelet RepresentationJournal of magnetic resonance imaging200011267278 V. Barra and J-Y Boire, Quantification of brain tissue volumes using MR/MR Fusion, Proceedings of the 22rd Annual EMBS International Conference, Chicago IL, July 23–28, (2000), pp.1451–1454,. K. Held, E-R. Kops, B-J. Krause et al., Markov Random Field Segmentation of Brain Images, IEEE Transactions on Medical Imaging, (1997), pp. 16878–886. ZhangDShenDMulti-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s diseaseNeuro Image201259895907 S. Saha, and S. Bandyopadhyay, MRI Brain Image Segmentation by Fuzzy Symmetry Based Genetic Clustering Technique, IEEE Congress on Evolutionary Computation (CEC) , (2007), pp.4417–4424. YuSTrancheventL-CLiuXGlanzelWSuykensJ ADe MoorBOptimized data fusion for kernel K-means clusteringPattern Anal. Mach. Intell., IEEE Trans201234510319 LászlóSSzilágyiM-SBenyóBBenyóZIntensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering modelsBiomedical Signal Processing and Control20116312 J-C. Bezdeck, R. Ehrlich and W. Full, FCM: Fuzzy c-means algorithm, Computers and Geoscience, (1984). S. Murugavalli and V. Rajamani, A High Speed Parallel Fuzzy C-Mean Algorithm for Brain Tumor Segmentation, in proc. of BIME Journal, December (2006), vol. 6, no. 1. NikhilNPalN-RPalS-KKellerJ-MBezdekJ-CA Possibilistic Fuzzy c-Means Clustering AlgorithmIEEE Transactions on Fuzzy Systems2005134517530 JiZXiaYSunQCaoGInterval-valued possibilistic fuzzy C means clustering algorithmFuzzy Sets and Systems2014253138156 Q. Mahmood, A. Chodorowski and A. Mehnert, A novel Bayesian approach to adaptive mean shift segmentation of brain images, Proc. 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Briandet and J-Y Boire, Fusion in Medical Imaging: Theory, Interests and Industrial Applications, MEDINFO, IOS Press, (2001), pp. 896–900. BalafarMFuzzy C-mean based brain MRI segmentation algorithmsArtifi. Intell. Rev20144134419 W. Cui, Y. Wang Yangyu Fan, Y. Feng and T. Lei, Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation, Int J. Biomed Imaging, (2013). doi: https://doi.org/10.1155/2013/930301 N. Mohamed, M. Ahmed and A. Farag, Modified fuzzy c-mean in medical image segmentation, Engineering in Medicine and Biology Society, Proceedings of the 20th Annual International Conference of the IEEE, (1998), pp. 3429–3432. AwateS-PTasdizenTFosterNWhitakerR-TAdaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classificationMed. Image. Anal200610726739 DunnJ-CA fuzzy relative of the ISODATA process and its use in detecting compact well-separated clustersJournal of Cybernetics2008333257 SuriJSinghSRedenLComputer vision and pattern recognition techniques for 2-D and 3-D cerebral cortical segmentation (part-I): A state of the art reviewPattern analysis and application2002514647 ZadehL-AFuzzy setsInformation and Control196583338353 A. Colin and J-Y Boire, MRI-SPECT image fusion for the synthesis of high resolution functional images: a prospective study, Proceedings - 19th Int. Conference - IEEE/EMBS, Chicago, IL. USA, Oct. 30–Nov. 2, (1997), pp.499–501. S-V. Aruna Kumar, B-S. Harish, V-N. Manjunath Aradhya, A picture fuzzy clustering approach for brain tumor Segmentation, Cognitive Computing and Information Processing (CCIP), Second Int. Conference on, (2016), pp. 12–13. doi: https://doi.org/10.1109/CCIP.2016.7802852 PalN-RPalS-KA review on image segmentation techniquesPattern Recognition199326912771294 J-C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Acad. Publ., Norwell, MA, USA (1981). DenœuxTMassonM-HEVCLUS: evidential clustering of proximity dataIEEE Trans. Syst. Man Cybern20043495109 RuspiniE-HA new approach to clusteringInformation and Control19691512232 N-E. El Harchaoui, M. Ait Kerroum, A. Hammouch, M. Ouadou and D. Aboutajdine, Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI, Computational Intelligence and Neuroscience, (2013). https://doi.org/10.1155/2013/435497 KrishnapuramRKellerJ-MA possibilistic approach to clusteringInternational Journal of Fuzzy Systems19932298110 PhamD-LPrinceJ-LAn adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneitiesPattern Recognition Letters19992015768 L-A. Zadeh, Fuzzy sets and their application to pattern classification and clustering analysis, in Classification and Clustering, J. V. Ryzin, Ed., (1977), pp. 251–282. Singh PKN-S Karan SikkaMishraA-KA fully automated algorithm under modified FCM framework for improved brain MR image segmentationMagn Reson Imaging2009279941004 |
| References_xml | – reference: Q. Mahmood, A. Chodorowski and A. Mehnert, A novel Bayesian approach to adaptive mean shift segmentation of brain images, Proc. IEEE Symposium on Computer-Based Medical Systems (CBMS), Rome, 20–22 June, (2012). https://doi.org/10.1109/CBMS.2012.6266304 – reference: S-Z. Beevi, M-M. Sathik and K. Senthamaraikannan, A Robust Fuzzy Clustering Technique with Spatial Neighborhood Information for Effective Medical Image Segmentation, Int. Journal of Computer Science and Information Security, vol. 7(3) (2010). – reference: AwateS-PTasdizenTFosterNWhitakerR-TAdaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classificationMed. Image. Anal200610726739 – reference: YangXFeiBA multiscale and multiblock fuzzy Cmeans classification method for brain MR imagesMed. Phys20113862879 – reference: DunnJ-CA fuzzy relative of the ISODATA process and its use in detecting compact well-separated clustersJournal of Cybernetics2008333257 – reference: García-LorenzoDFrancisSNarayananSArnoldD LCollinsD LouisReview of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imagingMed. Image Anal2013171118 – reference: A. Colin and J-Y Boire, MRI-SPECT image fusion for the synthesis of high resolution functional images: a prospective study, Proceedings - 19th Int. Conference - IEEE/EMBS, Chicago, IL. USA, Oct. 30–Nov. 2, (1997), pp.499–501. – reference: N. Mohamed, M. Ahmed and A. Farag, Modified fuzzy c-mean in medical image segmentation, Engineering in Medicine and Biology Society, Proceedings of the 20th Annual International Conference of the IEEE, (1998), pp. 3429–3432. – reference: DenœuxTMassonM-HEVCLUS: evidential clustering of proximity dataIEEE Trans. Syst. Man Cybern20043495109 – reference: AnejaDRawatI-J Tarun KumarFuzzy Clustering Algorithms for Effective Medical Image SegmentationIntelligent Systems and Applications2013115561 – reference: ZhangDShenDMulti-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s diseaseNeuro Image201259895907 – reference: S-V. Aruna Kumar, B-S. Harish, V-N. Manjunath Aradhya, A picture fuzzy clustering approach for brain tumor Segmentation, Cognitive Computing and Information Processing (CCIP), Second Int. Conference on, (2016), pp. 12–13. doi: https://doi.org/10.1109/CCIP.2016.7802852 – reference: BezdekJ-CHallL-OClarkMGoldofDClarkeL-PMedical image analysis with fuzzy modelsStat. Methods Med. Res19976191214 – reference: Ferreira da SilvaA-RBayesian mixture models of variable dimension for image segmentationComput Methods Programs Biomed200994114 – reference: S. Shen, W. Sandham, M. Granat, and A. Sterr, MRI Fuzzy Segmentation of Brain Tissue using Neighborhood Attraction with Neural-Network Optimization, IEEE Transaction on Information Technology in Biomedicine, (2005), vol. 9(3). – reference: BalafarMFuzzy C-mean based brain MRI segmentation algorithmsArtifi. Intell. Rev20144134419 – reference: NikhilNPalN-RPalS-KKellerJ-MBezdekJ-CA Possibilistic Fuzzy c-Means Clustering AlgorithmIEEE Transactions on Fuzzy Systems2005134517530 – reference: D-L. Pham, Fuzzy clustering with spatial constraints In. Presented at IEEE proceedings of the international conference image processing, (2002), pp 65–68. – reference: W. Cui, Y. Wang Yangyu Fan, Y. Feng and T. Lei, Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation, Int J. Biomed Imaging, (2013). doi: https://doi.org/10.1155/2013/930301 – reference: BricqSColletC-HArmspachJ-PUnifying framework for multimodal brain MRI segmentation based on Hidden Markov ChainsMed. Image. Anal200812639652 – reference: R. Harmouche, L. Collins, D. Arnold, S. Francis and T. Arbel, Bayesian MS lesion classification modeling regional and local spatial information, in The 18th International Conference on Pattern Recognition (ICPR), Hong Kong, IEEE, (2006), pp. 984–7. – reference: HaralickR-MShapiroL-GSurvey: image segmentation techniquesComputer Vision, Graphics, and Image Processing198529100132 – reference: PhamD-LPrinceJ-LAn adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneitiesPattern Recognition Letters19992015768 – reference: L-A. Zadeh, Fuzzy sets and their application to pattern classification and clustering analysis, in Classification and Clustering, J. V. Ryzin, Ed., (1977), pp. 251–282. – reference: L. Spirkovska, A summary of image segmentation techniques, NASA Technical Memorandum, (1993), pp. 1–11. – reference: J-C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Acad. Publ., Norwell, MA, USA (1981). – reference: VasudaPImproved Fuzzy C-Means Algorithm for MR Brain Image SegmentationInt. Journal on Computer Science and Engineering2010020517131715 – reference: ZexuanXYongQChenQ-NFeng. Fuzzy c-means clustering with weighted image patch for image segmentationApplied Soft Computing20121216591667 – reference: Bala-GanesanTSukaneshRSegmentation of Brain MR Images using Fuzzy Clustering Method with Sillhouette MethodJournal on Engineering and Applied Sciences2008310792795 – reference: RuspiniE-HA new approach to clusteringInformation and Control19691512232 – reference: LászlóSSzilágyiM-SBenyóBBenyóZIntensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering modelsBiomedical Signal Processing and Control20116312 – reference: KrishnapuramRKellerJ-MThe possibilistic c-means algorithm: Insights and recommendationIEEE Transactions on Fuzzy Systems199643385396 – reference: M. Shasidhar, V. Sudheer Raja and B. Vijay Kumar, MRI Brain Image Segmentation Using Modified Fuzzy C-Means Clustering Algorithm, Communication Systems and Network Technologies (CSNT), Int. Conference on. (2011), pp. 473–478. doi: https://doi.org/10.1109/CSNT.2011.102 – reference: S. Saha, and S. Bandyopadhyay, MRI Brain Image Segmentation by Fuzzy Symmetry Based Genetic Clustering Technique, IEEE Congress on Evolutionary Computation (CEC) , (2007), pp.4417–4424. – reference: K. Xiao, S. Hock Ho and A. Bargiela, Automatic Brain MRI Segmentation Scheme Based on Feature Weighting Factors Selection on Fuzzy C-Means Clustering Algorithms with Gaussian Smoothing, International Journal of Computer Intelligence in Bioinformatics and Systems Biology, (2010), vol. 1(3). – reference: JackCJr.BernsteinMFoxNThompsonPThe alzheimer’s disease neuroimaging initiative (adni): Mri methodsJ Magn Reson Imaging200827685691 – reference: CaldairouBNicolasPPiotrHColinSFrancoisRA non-local fuzzy segmentation method: application to brain MRIPattern Recognition20114419161927 – reference: G-C. Karmakar, S-L. Dooley and S-M. Rahman, Review on fuzzy image segmentation techniques in Design and Management of Multimedia Information Systems: Opportunities and Challenges, Idea Group Publishing, USA, (2001), pp. 282–313. – reference: MazouziSBatoucheMRange Image Segmentation Improvement by Fuzzy Edge Regularizationproc. of Information Technology Journal2008718490 – reference: ZadehL-AFuzzy setsInformation and Control196583338353 – reference: V. Barra and J-Y Boire, Quantification of brain tissue volumes using MR/MR Fusion, Proceedings of the 22rd Annual EMBS International Conference, Chicago IL, July 23–28, (2000), pp.1451–1454,. – reference: BarraVBoireJ-YTissue Segmentation on MR Images of the Brain by Possibilistic Clustering on a 3D Wavelet RepresentationJournal of magnetic resonance imaging200011267278 – reference: S. Murugavalli and V. Rajamani, A High Speed Parallel Fuzzy C-Mean Algorithm for Brain Tumor Segmentation, in proc. of BIME Journal, December (2006), vol. 6, no. 1. – reference: R. Krishnapuram, Generation of membership functions via possibilistic clustering, IEEE World Congress on Computational Intelligence, (1994), USA, pp. 902–908. – reference: ZadehL-AFuzzy sets as a basis for a theory of possibilityFuzzy Sets and Systems19781328 – reference: Singh PKN-S Karan SikkaMishraA-KA fully automated algorithm under modified FCM framework for improved brain MR image segmentationMagn Reson Imaging2009279941004 – reference: D. Kang, S. Y. Shin, C. O. Sung, J. Y. Kim, J.-K. Pack and H. D. Choi, “An improved method of breast MRI segmentation with Simplified K-means clustered images,” in Proceedings of the 2011 ACM Symposium on Research in Applied Computation, Miami, FL, Oct. 1922, pp. 226–31, 2011. – reference: D-E. Goldberg, Algorithmes génétiques : Exploration, optimisation et apprentissage automatique, Addison Wesley, (1997). – reference: K. Held, E-R. Kops, B-J. Krause et al., Markov Random Field Segmentation of Brain Images, IEEE Transactions on Medical Imaging, (1997), pp. 16878–886. – reference: KrishnapuramRKellerJ-MA possibilistic approach to clusteringInternational Journal of Fuzzy Systems19932298110 – reference: V. Barra, P. Briandet and J-Y Boire, Fusion in Medical Imaging: Theory, Interests and Industrial Applications, MEDINFO, IOS Press, (2001), pp. 896–900. – reference: SuriJSinghSRedenLComputer vision and pattern recognition techniques for 2-D and 3-D cerebral cortical segmentation (part-I): A state of the art reviewPattern analysis and application2002514647 – reference: CaiWChenSZhangDFast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentationPattern Recognition200740825838 – reference: PalN-RPalS-KA review on image segmentation techniquesPattern Recognition199326912771294 – reference: J-C. Bezdeck, R. Ehrlich and W. Full, FCM: Fuzzy c-means algorithm, Computers and Geoscience, (1984). – reference: J. Yu and Y. Wang, Molecular Image Segmentation Based on Improved Fuzzy Clustering, in proc. of International Journal on Biomedical Imaging, Jan. (2007), no. 1. – reference: JiZXiaYSunQCaoGInterval-valued possibilistic fuzzy C means clustering algorithmFuzzy Sets and Systems2014253138156 – reference: GroverNA study of various fuzzy clustering algorithmsInternational Journal of Engineering Research201433177181 – reference: YuSTrancheventL-CLiuXGlanzelWSuykensJ ADe MoorBOptimized data fusion for kernel K-means clusteringPattern Anal. Mach. Intell., IEEE Trans201234510319 – reference: N-E. El Harchaoui, M. Ait Kerroum, A. Hammouch, M. Ouadou and D. Aboutajdine, Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI, Computational Intelligence and Neuroscience, (2013). https://doi.org/10.1155/2013/435497 |
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| SubjectTerms | Alzheimer’s disease Brain tissue clustering Fuzzy c-means algorithm Genetic algorithms Hybrid reasoning Possibilistic c-means algorithm Research Article |
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| Title | Improvement of CSF, WM and GM Tissue Segmentation by Hybrid Fuzzy — Possibilistic Clustering Model based on Genetic Optimization Case Study on Brain Tissues of Patients with Alzheimer’s Disease |
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