Segmentation of MRI data using multi-objective antlion based improved fuzzy c-means

• Autorzy: Singh Munendra , Venkatesh Vishal , Verma Ashish , Sharma Neeraj

Identyfikatory

DOI

10.1016/j.bbe.2020.07.001

• Języki publikacji: EN

Abstrakty

EN

Accurate segmentation of brain tissues in magnetic resonance imaging (MRI) data plays critical role in the clinical diagnostic and treatment planning. The presence of noise and artifacts in MRI data degrades the performance of segmentation algorithms. In this view, the present study proposes a complete unsupervised clustering based multi-objective modified fuzzy c-mean (MOFCM) segmentation algorithm, which inculcates multi-objective antlion optimization (MOALO) to minimize the cluster compactness and fuzzy hyper-volume fitness functions. The output segmented image corresponds to minimum value of partition entropy in the obtained solution set. The present study integrates proposed MOFCM with a new cluster number validity index, which allows user not to provide number of segments in image as an input. The proposed MOFCM algorithm is extensively validated on seventy two synthetic images corrupted with different levels of Gaussian, Speckle and Rician noises, forty simulated BrainWeb MRI images suffered from noise and inhomogeneity, and 10 real IBSR MRI dataset of images. The results are compared with existing popular clustering based algorithms, and supervised deep learning based algorithms, i.e. UNet, SegNet and Quick- NAT. The proposed MOFCM algorithm demonstrate the superior segmentation performance in comparison to popular FCM based clustering algorithms, SegNet and UNet, whereas the segmentation results of proposed MOFCM are at par with QuickNAT.

Słowa kluczowe

EN

robust; image segmentation; multiobjective antlion optimization; fuzzy c means; magnetic resonance imaging data

PL

segmentacja obrazu; optymalizacja antlion; logika rozmyta; rezonans magnetyczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1250--1266
• Opis fizyczny: Bibliogr. 57 poz., rys., tab., wykr.

Twórcy

autor

Singh Munendra

• Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

autor

Venkatesh Vishal

• Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

autor

Verma Ashish

• School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India

autor

Sharma Neeraj

• Department of Radiodiagnosis and Imaging, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India

Bibliografia

• [1] Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 2002;21:193–9.
• [2] Aja-Fernández S, Alberola-López C, Westin CF. Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach. IEEE Trans Image Process 2008;17:1383–98.
• [3] Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39:2481–95.
• [4] Bal A, Banerjee M, Chakrabarti A, Sharma P. MRI brain tumor segmentation and analysis using rough-fuzzy c-means and shape based properties. J King Saud Univ Comput Inform Sci 2018.
• [5] Balafar M. Fuzzy c-mean based brain MRI segmentation algorithms. Artif Intell Rev 2014;41:441–9.
• [6] Bandyopadhyay S, Maulik U, Mukhopadhyay A. Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Trans Geosci Rem Sens 2007;45:1506–11.
• [7] Benaichouche AN, Oulhadj H, Siarry P. Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, mahalanobis distance and post-segmentation correction. Digital Signal Process 2013;23:1390–400.
• [8] Benaichouche AN, Oulhadj H, Siarry P. Multiobjective improved spatial fuzzy c-means clustering for image segmentation combining pareto-optimal clusters. J Heurist 2016;22:383–404.
• [9] Bezdek JC, Ehrlich R, Full W. FCM: the fuzzy c-means clustering algorithm. Comput Geosci 1984;10:191–203.
• [10] Billaudel P, Devillez A, Lecolier GV. Performance evaluation of fuzzy classification methods designed for real time application. Int J Approx Reason 1999;20:1–20.
• [11] Chen S, Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern Part B Cybern 2004;34:1907–16.
• [12] Choudhry MS, Kapoor R. Performance analysis of fuzzy c-means clustering methods for MRI image segmentation. Proc Comput Sci 2016;89:749–58.
• [13] Cocosco CA, Kollokian V, Kwan RKS, Pike GB, Evans AC. Brainweb: Online interface to a 3d MRI simulated brain database. NeuroImage; 1997.
• [14] Cui S, Mao L, Jiang J, Liu C, Xiong S. Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J Healthc Eng 2018.
• [15] Dave RN. Validating fuzzy partitions obtained through c-shells clustering. Pattern Recogn Lett 1996;17:613–23.
• [16] Faceli K, de Souto MC, de Carvalho AC. A strategy for the selection of solutions of the pareto front approximation in multi-objective clustering approaches. 2008 10th Brazilian Symposium on Neural Networks. IEEE; 2008. p. 27–32.
• [17] Forouzanfar M, Forghani N, Teshnehlab M. Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Eng Appl Artif Intell 2010;23:160–8.
• [18] Gath I, Geva AB. Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 1989;11:773–80.
• [19] Jalab HA, Hasan A. magnetic resonance imaging segmentation techniques of brain tumors: a review. Arch Neurosci 2019;6.
• [20] Jamshidi O, Pilevar AH. Automatic segmentation of medical images using fuzzy c-means and the genetic algorithm. J Comput Med 2013.
• [21] Kalaiselvi T, Nagaraja P, Karthick VG. Improved fuzzy c-means for brain tissue segmentation using t1-weighted MRI head scans. Int J Innov Sci Eng Technol 2016;3.
• [22] Kaur T, Saini BS, Gupta S. An optimal spectroscopic feature fusion strategy for MR brain tumor classification using fisher criteria and parameter-free bat optimization algorithm. Biocybern Biomed Eng 2018;38:409–24.
• [23] Khotanlou H, Colliot O, Atif J, Bloch I. 3d brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 2009;160:1457–73.
• [24] Krinidis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 2010;19:1328–37.
• [25] Lazli L, Boukadoum M. 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. Int J Networked Distrib Comput 2018;6:63–77.
• [26] Lei T, Jia X, Zhang Y, He L, Meng H, Nandi AK. Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans Fuzzy Syst 2018;26:3027–41.
• [27] Mekhmoukh A, Mokrani K. Improved fuzzy c-means based particle swarm optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Comput Methods Programs Biomed 2015;122:266–81.
• [28] Mirjalili S. The ant lion optimizer. Adv Eng Softw 2015;83:80–98.
• [29] Mirjalili S, Jangir P, Saremi S. Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 2017;46:79–95.
• [30] Mohan G, Subashini MM. Mri based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control 2018;39:139–61.
• [31] Mukhopadhyay A, Bandyopadhyay S, Maulik U. Clustering using multi-objective genetic algorithm and its application to image segmentation. 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE; 2006. p. 2678–83.
• [32] Mukhopadhyay A, Maulik U, Bandyopadhyay S. Multiobjective genetic clustering with ensemble among pareto front solutions: application to MRI brain image segmentation. 2009 Seventh International Conference on Advances in Pattern Recognition. IEEE; 2009. p. 236–9.
• [33] Myronenko A. 3d MRI brain tumor segmentation using autoencoder regularization. International MICCAI Brainlesion Workshop. Springer; 2018. p. 311–20.
• [34] Narayanan A, Rajasekaran MP, Zhang Y, Govindaraj V, Thiyagarajan A. Multi-channeled MR brain image segmentation: a novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern Biomed Eng 2019;39:350–81.
• [35] Pakhira MK, Bandyopadhyay S, Maulik U. Validity index for crisp and fuzzy clusters. Pattern Recogn 2004;37:487–501.
• [36] Pham TX, Siarry P, Oulhadj H. A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods. Magn Reson Imaging 2019;61:41–65.
• [37] Raju AR, Suresh P, Rao RR. Bayesian HCS-based multi-svnn: a classification approach for brain tumor segmentation and classification using bayesian fuzzy clustering. Biocybern Biomed Eng 2018;38:646–60.
• [38] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer; 2015. p. 234–41.
• [39] Roy AG, Conjeti S, Navab N, Wachinger C, Initiative ADN, et al. Quicknat: a fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage 2019;186:713–27.
• [40] Sharma N, Ray AK, Sharma S, Shukla K, Pradhan S, Aggarwal LM. Segmentation and classification of medical images using texture-primitive features: application of bam-type artificial neural network. J Med Phys/Assoc Med Phys India 2008;33:119.
• [41] Shree NV, Kumar T. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Informatics 2018;5:23–30.
• [42] Singh M, Bhattacharjee R, Sharma N, Verma A. An improved Xie-Beni index for cluster validity measure. 2017 Fourth International Conference on Image Information Processing (ICIIP). IEEE; 2017. p. 1–5.
• [43] Singh M, Verma A, Sharma N. Optimized multistable stochastic resonance for the enhancement of pituitary microadenoma in MRI. IEEE J Biomed Health Informatics 2017;22:862–73.
• [44] Sriramakrishnan P, Kalaiselvi T, Rajeswaran R. Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi- sequence scans with GPU CUDA machine. Biocybern Biomed Eng 2019;39:470–87.
• [45] Sriramakrishnan P, Kalaiselvi T, Somasundaram K, Rajeswaran R. A rapid knowledge-based partial supervision fuzzy c-means for brain tissue segmentation with cuda-enabled GPU machine. Int J Imaging Syst Technol 2019;29:547–60.
• [46] Sumathi R, Venkatesulu M, Arjunan SP. Extracting tumor in MR brain and breast image with Kapur's entropy based cuckoo search optimization and morphological reconstruction filters. Biocybern Biomed Eng 2018;38: 918–30.
• [47] Sun DW. Computer vision technology for food quality evaluation. Academic Press; 2016.
• [48] Szilagyi L, Benyo Z, Szilágyi SM, Adam H. MR brain image segmentation using an enhanced fuzzy c-means algorithm. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439). IEEE; 2003. p. 724–6.
• [49] Tong J, Zhao Y, Zhang P, Chen L, Jiang L. MRI brain tumor segmentation based on texture features and kernel sparse coding. Biomed Signal Process Control 2019;47:387–92.
• [50] Trauwaert E, Rousseeuw P, Kaufman L. Fuzzy clustering by minimizing the total hypervolume. Information and Classification. Springer; 1993. p. 61–71.
• [51] Wang W, Zhang Y. On fuzzy cluster validity indices. Fuzzy sets and systems 2007;158:2095–117.
• [52] Xu S, Hu L, Yang X, Liu X. A cluster number adaptive fuzzy c-means algorithm for image segmentation. Int J Signal Process Image Process Pattern Recogn 2013;6:191–204.
• [53] Yang T, Song J, Li L. A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI. Biocybern Biomed Eng 2019;39:613–23.
• [54] Zanaty E. Determining the number of clusters for kernelized fuzzy c-means algorithms for automatic medical image segmentation. Egypt Informatics J 2012;13:39–58.
• [55] Zhang C, Shen X, Cheng H, Qian Q. Brain tumor segmentation based on hybrid clustering and morphological operations. Int J Biomed Imaging 2019.
• [56] Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y. A deep learning model integrating FCNNS and CRFS for brain tumor segmentation. Med Image Anal 2018;43:98–111.
• [57] Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, et al. Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad Radiol 2004;11:178–89.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).