Abstract
Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation. Clustering also plays a major role in medical image processing. Manual image segmentation is very tedious and time consuming task and the results of manual segmentation are subjected to errors due to huge and varying data. Therefore, automated segmentation systems are gaining enormous importance nowadays. This paper presents an automated system for segmentation of brain tissues namely white matter, gray matter and cerebrospinal fluid from brain MRI images. In this work, we propose a novel clustering approach, Fuzzy-Gravitational Search Algorithm(GSA) for MRI brain image segmentation. The proposed approach is based on GSA, and uses fuzzy inference rules for controlling the parameter α as search progresses. The results of the system are compared with GSA and recent work on brain image segmentation algorithms for both real and simulated database on the basis of Dice Coefficient values. The performance of the Fuzzy-GSA algorithm is also evaluated against four benchmark datasets from the UC Irvine repository. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering over the selected datasets when compared with several other clustering algorithms.
Similar content being viewed by others
References
Al-Sultan KS (1995) A tabu search approach to the clustering problem. Pattern Recogn 28(9):1443–1451
Benaichouche AN, Oulhadj H, Siarry P (2013) Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digit Signal Process 23(5):1390-1400, ISSN 1051-2004. https://doi.org/10.1016/j.dsp.2013.07.005
Blake CL, Merz CJ UCI repository of machine learning databases. Available from: http://www.ics.uci.edu/-mlearn/MLRepository.html
Chen Y, Zhang J, Wang S, Zheng Y (2012) Brain magnetic resonance image segmentation based on an adapted non-local fuzzy c-means method. IET Comput Vis 6(6):610-625. https://doi.org/10.1049/iet-cvi.2011.0263
Chen Y, Li J, Zhang H, Zheng Y, Jeon B, Wu QJ (2016) Non-local-based spatially constrained hierarchical fuzzy C-means method for brain magnetic resonance imaging segmentation. IET Image Process 10(11):865-876, 11. https://doi.org/10.1049/iet-ipr.2016.0271
Ching-Yi C, Fun Y (2004) Particle swarm optimization algorithm and its application to clustering analysis. In: IEEE International Conference on Networking, Sensing and Control
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302
Fathian M, Amiri B, Maroosi A (2007) Application of honey-bee mating optimization algorithm on clustering. Appl Math Comput 190(2):1502–1513
Forgy EW (1965) Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21(1965):768–769
Hanuman V, Agrawal RK, Sharan A (2016) An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Appl Soft Comput 46:543-557, ISSN 1568-4946. https://doi.org/10.1016/j.asoc.2015.12.022
Hatamlou A, Abdullah S, Nezamabadi-pour H (2011) Application of gravitational search algorithm on data clustering. Rough Sets and Knowledge Technology, pp 337–346
Hatamlou A, Abdullah S, Nezamabadi-pour H (2012) A combined approach for clustering based on K-means and gravitational search algorithms. Swarm Evol Comput 6:47–52S
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31(8):651–666
Jardine N, van Rijsbergen CJ (1971) The use of hierarchic clustering in information retrieval. Inf Storage Retr 7(5):217–240
Kalaiselvi T, Nagaraja P, Karthick Ganapathy V (2016) Improved Fuzzy C-means for brain tissue segmentation using T1- weighted MRI head scans. International Journal of Innovative Science, Engineering & Technology 3(Online):2348–7968
Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York
Kerr G, Ruskin HJ, Crane M, Doolan P (2008) Techniques for clustering gene expression data. Comput Biol Med 38(3):283–293
Liao L, Lin T, Li B (2008) MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognit Lett 29(10):1580–1588
MacQueen (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol 1, pp 281-297
Mahmood Q, Chodorowski A, Persson M (2015) Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps. IRBM 36(3):185-196, ISSN 1959-0318. https://doi.org/10.1016/j.irbm.2015.01.007
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33(9):1455–1465
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Išgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5):1252–1261. https://doi.org/10.1109/TMI.2016.2548501
Nakib A, Oulhadj H (2009) A thresholding method based on two-dimensional fractional differentiation. Image Vis Comput 27:1343–1357
Namburu A, Samayamantula SK, Edara SR. Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation. IET Image Process 11(9):777–785
Noback CR, Strominger NL, Demarest RJ, Ruggiero DA (2005) The human nervous system: structure and function, 6th edn. Humana Press, Totowa
Om PV, Heena H. A novel intuitionistic Fuzzy co-clustering algorithm for brain images. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09320-8
Ortiz A, Górriz JM, Ramírez J, Salas-Gonzalez D (2011) MR brain image segmentation by growing hierarchical SOM and probability clustering. Electron Lett 47(10):585-586. https://doi.org/10.1049/el.2011.0322
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Saglam B, Türkay M, Salman FS, Sayın S, Karaesmen F, Örmeci EL (2006) A mixed-integer programming approach to the clustering problem with an application in customer segmentation. Eur J Oper Res 173(3):866–879
Saneipour K, Mohammadpoor M (2019) Improvement of MRI brain image segmentation using Fuzzy unsupervised learning. Iran J Radiol. In Press. https://doi.org/10.5812/iranjradiol.69063,
Selim SZ, Alsultan K (1991) A simulated annealing algorithm for the clustering problem. Pattern Recognit 24(10):1003–1008
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195
Sudipta Roy SK, Bandyopadhyay (2016) A new method of brain tissues segmentation from MRI with accuracy estimation. Procedia Comput Sci 85:362-369, ISSN 1877-0509. https://doi.org/10.1016/j.procs.2016.05.244
Sung CS, Jin HW (2000) A tabu-search-based heuristic for clustering. Pattern Recogn 33(5):849–858
Tombros A, Villa R, van Rijsbergen CJ (2002) The effectiveness of query-specific hierarchic clustering in information retrieval. Inf Process Manag 38(4):559–582
Wahba M (2008) An Automated Modified Region Growing Technique for Prostate Segmentation in Trans-Rectal Ultrasound Images, Master’s Thesis, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
Xia Y, Fenga D, Wangd T, Zhaob R, Zhangb Y (2007) Image segmentation by clustering of spatial patterns. Pattern Recognit Lett 28(12):1548–1555
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hooda, H., Verma, O.P. Fuzzy clustering using gravitational search algorithm for brain image segmentation. Multimed Tools Appl 81, 29633–29652 (2022). https://doi.org/10.1007/s11042-022-12336-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12336-x