A hybrid harmony search algorithm for MRI brain segmentation

Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length encoding in each harmony memory vector, this algorithm is able to represent variable numbers of candidate cluster centers at each iteration. A new HS operator, called the “empty operator”, has been introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. Evaluation of the proposed algorithm has been performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results show the ability of this algorithm to find the appropriate number of naturally occurring regions in brain images. Furthermore, the superiority of the proposed algorithm over various state-of-the-art segmentation algorithms is demonstrated quantitatively.

[1]  Jing Bai,et al.  Atlas-Based Fuzzy Connectedness Segmentation and Intensity Nonuniformity Correction Applied to Brain MRI , 2007, IEEE Transactions on Biomedical Engineering.

[2]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[3]  Hong Yan,et al.  Attractable snakes based on the greedy algorithm for contour extraction , 2002, Pattern Recognit..

[4]  Ujjwal Maulik,et al.  Validity index for crisp and fuzzy clusters , 2004, Pattern Recognit..

[5]  Hong Yan,et al.  Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images , 2006 .

[6]  Zong Woo Geem,et al.  State-of-the-Art in the Structure of Harmony Search Algorithm , 2010, Recent Advances In Harmony Search Algorithm.

[7]  Zong Woo Geem,et al.  Music-Inspired Harmony Search Algorithm , 2009 .

[8]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[9]  Ajith Abraham,et al.  Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications , 2009, Foundations of Computational Intelligence.

[10]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[11]  Zong Woo Geem,et al.  Harmony Search for Generalized Orienteering Problem: Best Touring in China , 2005, ICNC.

[12]  M. Fesanghary,et al.  Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm , 2009 .

[13]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[14]  Z W Geem,et al.  APPLICATION OF HARMONY SEARCH TO MULTI-OBJECTIVE OPTIMIZATION FOR SATELLITE HEAT PIPE DESIGN , 2006 .

[15]  Ujjwal Maulik,et al.  Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[16]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[17]  Lawrence O. Hall,et al.  A Scalable Framework For Segmenting Magnetic Resonance Images , 2009, J. Signal Process. Syst..

[18]  Klaus D. Tönnies,et al.  Segmentation of medical images using adaptive region growing , 2001, SPIE Medical Imaging.

[19]  J Sijbers,et al.  Watershed-based segmentation of 3D MR data for volume quantization. , 1997, Magnetic resonance imaging.

[20]  Zong Woo Geem,et al.  Optimal Scheduling of Multiple Dam System Using Harmony Search Algorithm , 2007, IWANN.

[21]  Zong Woo Geem,et al.  Harmony Search Algorithms for Structural Design Optimization , 2009 .

[22]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[23]  Xiao Zhi Gao,et al.  A Hybrid Optimization Method for Fuzzy Classification Systems , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[24]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[25]  Emanuel Falkenauer,et al.  Genetic Algorithms and Grouping Problems , 1998 .

[26]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[27]  Juan Zhou,et al.  Fuzzy approach to incorporate hemodynamic variability and contextual information for detection of brain activation , 2008, Neurocomputing.

[28]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  Mohammed Azmi Al-Betar,et al.  A harmony search algorithm for university course timetabling , 2010, Annals of Operations Research.

[31]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues , 2009, Foundations of Computational Intelligence.

[32]  Daniel Withey,et al.  A Review of Medical Image Segmentation: Methods and Available Software , 2008 .

[33]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[34]  Jean-Marc Constans,et al.  A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images , 2007, Image Vis. Comput..

[35]  Taiyi Zhang,et al.  Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory , 2008, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[36]  Aditya Panchal,et al.  Harmony Search in Therapeutic Medical Physics , 2009 .

[37]  F.E.Z. Abou-Chadi,et al.  Automatic segmentation and labeling of human brain tissue from MR images , 2000, Proceedings of the Seventeenth National Radio Science Conference. 17th NRSC'2000 (IEEE Cat. No.00EX396).

[38]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[39]  Sanghamitra Bandyopadhyay,et al.  A New Line Symmetry Distance and Its Application to Data Clustering , 2009, Journal of Computer Science and Technology.

[40]  Ricardo J. G. B. Campello,et al.  On the efficiency of evolutionary fuzzy clustering , 2009, J. Heuristics.

[41]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[42]  I. Guyon,et al.  Detecting stable clusters using principal component analysis. , 2003, Methods in molecular biology.

[43]  Hassan Abolhassani,et al.  Harmony K-means algorithm for document clustering , 2009, Data Mining and Knowledge Discovery.

[44]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  Sanghamitra Bandyopadhyay,et al.  A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[46]  Mandava Rajeswari,et al.  A Novel Image Segmentation Algorithm Based on Harmony Fuzzy Search Algorithm , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[47]  Zhigang Peng,et al.  SEGMENTATION OF WHITE MATTER, GRAY MATTER, AND CSF FROM MR BRAIN IMAGES AND EXTRACTION OF VERTEBRAE FROM MR SPINAL IMAGES , 2006 .

[48]  Manuel Graña,et al.  An adaptive field rule for non-parametric MRI intensity inhomogeneity estimation algorithm , 2009, Neurocomputing.

[49]  Abdul Rahman Ramli,et al.  Review of brain MRI image segmentation methods , 2010, Artificial Intelligence Review.

[50]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[51]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[52]  S.M. Szilagyi,et al.  MR brain image segmentation using an enhanced fuzzy C-means algorithm , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[53]  Tonghua Zhang,et al.  Overview of Applications and Developments in the Harmony Search Algorithm , 2009 .

[54]  M. Tamer Ayvaz,et al.  Application of Harmony Search algorithm to the solution of groundwater management models , 2009 .

[55]  Ujjwal Maulik,et al.  A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification , 2005, Fuzzy Sets Syst..

[56]  Zong Woo Geem,et al.  Harmony Search Algorithm for Solving Sudoku , 2007, KES.

[57]  Verónica Médina-Bañuelos,et al.  Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information , 2006, IEEE Transactions on Medical Imaging.

[58]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[59]  Raymond Chiong,et al.  Nature That Breeds Solutions , 2012, Int. J. Signs Semiot. Syst..

[60]  Raymond Chiong,et al.  Nature-Inspired Algorithms for Optimisation , 2009, Nature-Inspired Algorithms for Optimisation.

[61]  Zong Woo Geem,et al.  Music Composition Using Harmony Search Algorithm , 2009, EvoWorkshops.

[62]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[63]  M. Fesanghary,et al.  Combined heat and power economic dispatch by harmony search algorithm , 2007 .

[64]  D. Ramachandram,et al.  Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[65]  M. Tamer Ayvaz,et al.  Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm , 2007 .

[66]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[67]  Hayit Greenspan,et al.  An Adaptive Mean-Shift Framework for MRI Brain Segmentation , 2009, IEEE Transactions on Medical Imaging.

[68]  Mandava Rajeswari,et al.  A hybrid harmony search algorithm for MRI brain segmentation , 2010, 9th IEEE International Conference on Cognitive Informatics (ICCI'10).

[69]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[70]  Liang Liao,et al.  MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach , 2008, Pattern Recognit. Lett..

[71]  Hong Yan,et al.  An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation , 2003, IEEE Transactions on Medical Imaging.

[72]  J.L. Marroquin,et al.  An accurate and efficient Bayesian method for automatic segmentation of brain MRI , 2002, IEEE Transactions on Medical Imaging.

[73]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[74]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[75]  M Ashtari,et al.  Computerized volume measurement of brain structure. , 1990, Investigative radiology.

[76]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[77]  Z. Geem.  Music-Inspired Harmony Search Algorithm: Theory and Applications , 2009 .

[78]  Dao-Qiang Zhang,et al.  A novel kernelized fuzzy C-means algorithm with application in medical image segmentation , 2004, Artif. Intell. Medicine.

[79]  Lawrence O. Hall,et al.  Using Fuzzy Information in Knowledge Guided Segmentation of Brain Tumors , 1995, Fuzzy Logic in Artificial Intelligence.

[80]  Z. Geem.  Particle-swarm harmony search for water network design , 2009 .

[81]  Zong Woo Geem,et al.  Ecological optimization using harmony search , 2008 .

[82]  W. Eric L. Grimson,et al.  Segmentation of brain tissue from magnetic resonance images , 1995, Medical Image Anal..

[83]  Dhanesh Ramachandram,et al.  Dynamic fuzzy clustering using Harmony Search with application to image segmentation , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[84]  Dhanesh Ramachandram,et al.  An Optimization Algorithm Based on Harmony Search for RNA Secondary Structure Prediction , 2010, Recent Advances In Harmony Search Algorithm.

[85]  Zong Woo Geem,et al.  Application of Harmony Search to Vehicle Routing , 2005 .

[86]  Milan Sonka,et al.  Knowledge-based interpretation of MR brain images , 1996, IEEE Trans. Medical Imaging.