Abstract  Image  segmentation  plays  a  major  role  in  medical  im aging  applications.  During  last  decades,  developing  robust  and  efficient  algorithms  for  medical  image  segmenta tion  has  been  a  demanding  area  of  growing  research  interest.  The  renowned  unsupervised  clustering  method,  Fuzzy  C-Me ans  (FCM)  algorithm  is  extensively  used  in  medical image  segmentation. Despite its pervasive use, convention al FCM is highly sensitive to noise because it segments images on the basis  of  intensity  values.  In  this  paper,  for  the  segment ation  of  noisy  medical  images,  an  effective  approac h  is  presented.  The  proposed  approach utilizes  histogram  based  Fuzzy  C- Means  clustering  algorithm  for  the  segmentation  of medical  images.  To  improve the robustness against noise, the spatial p robability of the neighboring pixels is integrated in the objective function of  FCM.  The  noisy  medical  images  are  denoised,  with  th e  help  of  an  effective  denoising  algorithm,  prior  to  segmentation,  to  increase  further  the  approach’s  robustness.  A  compa rative  analysis  is  done  between  the  conventional  FCM  and  the  proposed  approach.  The  results  obtained  from  the  experimenta tion  show  that  the  proposed  approach  attains  reliable  segmentation  accuracy  despite  of  noise  levels.  From  the  experime ntal  results,  it  is  also  clear  that  the  proposed  approach  is  more  efficient  and robust against noise when compared to that of t he FCM.   

References

[1] Ahmed M., Yamany S., Mohamed N., Farag A., and Moriarty T., A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability 81 Segmentation of MRI Data, Computer Journal of IEEE Transactions on Medical Imaging , vol. 21, no. 3, pp.193-199, 2002.

[2] Ayache N., Cinquin P., Cohen I., Cohen L., Leitner F., and Monga O., Segmentation of Complex Three Dimensional Medical Objects: A Challenge and a Requirement for Computer- Assisted Surgery Planning and Performance, in Taylor R., Lavallee S., Burdea G., and Mosges R., (Eds), Computer-Integrated Surgery: Technology and Clinical Applications, MIT Press, pp. 59-74, 1996.

[3] Balafar M., Ramli R., Saripan M., Mahmud R., Mashohor S., and Balafar M., New Multi-Scale Medical Image Segmentation Based on Fuzzy C- Mean (FCM), in Proceedings of IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications , Cyberjaya, pp. 66-70, 2008.

[4] Bezdek J., Pattern Recognition with Fuzzy Objective Function Algorithms , Plenum Press, New York, 1981.

[5] Bezdek J., Hall L., and Clarke L., Review of MR Image Segmentation Techniques Using Pattern Recognition, Computer Journal of Medical Physics , vol. 20, no. 4, pp.1033-1048, 1993.

[6] Chen S. and Zhang D., Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure, Computer Journal of IEEE Transactions on Systems, Man, and Cybernetics , vol. 34, no. 4, pp.1907 1916, 2004.

[7] Chuang K., Tzeng H., Chen S., Wua J., and Chen T., Fuzzy C-Means Clustering with Spatial Information for Image Segmentation, Computer Journal of Medical Imaging and Graphics , vol. 30, no. 1, pp. 9-15, 2006.

[8] Dawant B., Zijidenbos A., and Margolin R., Correction of Intensity Variations in MR Image for Computer-Aided Tissue Classification, IEEE Transactions on Medical Imaging , vol. 12, no. 4, pp. 770-781, 1993.

[9] Dong G. and Xie M., Color Clustering and Learning for Image Segmentation Based on Neural Networks, Compute IEEE Transactions on Neural Networks , vol. 16, no. 4, pp. 925-936, 2005.

[10] Du R. and Lee H., A Modified-FCM Segmentation Algorithm for Brain MR Images, in Proceedings of ACM International Conference on Hybrid Information Technology , Hubei, pp. 25-27, 2009.

[11] Dunn J., A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well Separated Clusters, Journal of Cybernetics , vol. 3, no. 3, pp. 32-57, 1974.

[12] Fan J., Zhen W., and Xie W., Suppressed Fuzzy C-Means Clustering Algorithm, Pattern Recognition Letters , vol. 24, no. 9-10, pp. 1607- 1612, 2003.

[13] Grimson W., Ettinger G., Kapur T., Leventon M., Wells W., and Kikinis R., Utilizing Segmented MRI Data in Image Guided Surgery, International Journal of Pattern Recognition and Artificial Intelligence , vol. 11, no. 8, pp. 1367- 1397, 1998.

[14] Haralick R. and Shaprio L., Image Segmentation Techniques, Computer Vision, Graphics and Image Processing , vol. 29, no. 1, pp. 100-132, 1985.

[15] Hsieh C., Kuo C., Chao C., and Lu P., Image Segmentation Based on Fuzzy Clustering Algorithm, IAPR Workshop on Machine Vision Applications , Japan, pp. 460-463, 1994.

[16] Huang Z., Xie Y., Liu D., and Hou L., Using Fuzzy C-means Cluster for Histogram-Based Color Image Segmentation, in Proceedings of the 2009 International Conference on Information Technology and Computer Science , Ukraine, pp. 597-600, 2009.

[17] Hung W., Yang M., and Chen D., Parameter Selection for Suppressed Fuzzy C-Means with an Application to MRI Segmentation, Pattern Recognition Letters , vol. 27, no. 5, pp. 424-438, 2006.

[18] Johnson B., Atkins M., Mackiewich B., and Andson M., Segmentation of Multiple Sclerosis Lesions in Intensity Corrected Multispectral MRI, IEEE Transactions on Medical Imaging , vol. 15, no. 2, pp. 154-169, 1996.

[19] Kannan S., Ramathilagam S., and Sathya A., Robust Fuzzy C-Means in Classifying Breast Tissue Regions, in Proceedings of ARTCOM International Conference on Advances in Recent Technologies in Communication and Computing , Kerala, pp. 543-545, 2009.

[20] Khoo V., Dearnaley D., Finnigan D., Padhani A., Tanner S., and Leach M., Magnetic Resonance Imaging (MRI): Considerations and Applications in Radiotherapy Treatment Planning, Radiotherapy and Oncology , vol. 42, no. 1, pp. 1- 15, 1997.

[21] Lai C. and Chang C., A Hierarchical Evolutionary Algorithm for Automatic Medical Image Segmentation, An International Journal Expert Systems with Applications , vol. 36, no. 1, pp. 248-259, 2009.

[22] Larie S. and Abukmeil S., Brain Abnormality in Schizophrenia: A Systematic and Quantitative Review of Volumetric Magnetic Resonance Imaging Studies, The British Journal of Psychiatry , vol. 172, no. 2, pp. 110-120, 1998.

[23] Liew A., Yan H., Law N., Image Segmentation Based on Adaptive Cluster Prototype 82 The International Arab Journal of Information Techn ology, Vol. 9, No. 1, January 2012 Estimation, IEEE Transactions on Fuzzy Systems , vol. 13, no. 4, pp. 444-453, 2005.

[24] Li M., Huang T., and Zhu G., Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation, in Proceedings of the WGEC Second International Conference on Genetic and Evolutionary Computing , Hubei, pp. 285-288, 2008.

[25] Lung H. and Kim J., A Generalized Spatial Fuzzy C-Means Algorithm for Medical Image Segmentation, in Proceedings of the 18 th International Conference on Fuzzy Systems , Jeju- Island, pp. 409-414, 2009.

[26] Mendes M. and Sacks L., Assessment of the Performance of Fuzzy Cluster Analysis in the Classification of RFC Documents, in Proceedings of the 2000 London Communications Symposium , London, pp. 1-5, 2000.

[27] Muller-Gartner H., Links J., Prince J., Bryan R., McVeigh E., Leal J., Davatzikos C. and Frost J., Measurement of Radiotracer Concentration in Brain Gray Matter using Positron Emission Tomography: MRI-Based Correction for Partial Volume Effects, Compute Journal of Cerebral Blood Flow and Metabolism , vol. 12, no. 4, pp. 571-583, 1992.

[28] Murugavalli S. and Rajamani V., A High Speed Parallel Fuzzy C-Mean Algorithm for Brain Tumor Segmentation, ICGST International Journal on Bioinformatics and Medical Engineering, vol. 6, no. 1, pp. 29-34, 2006.

[29] Noordam J., Van den Broek W., and Buydens L., Geometrically Guided Fuzzy C-Means Clustering for Multivariate Image Segmentation, in Proceedings of International Conference on Pattern Recognition, USA, pp. 462-465, 2000.

[30] Pal N. and Pal S., A Review on Image Segmentation Techniques, Pattern Recognition, vol. 26, no. 9, pp. 1277 1294, 1993.

[31] Pal N., Pal K., Keller J., and Bezdek J., A Possibilistic Fuzzy C-Means Clustering Algorithm, IEEE Transactions on Fuzzy Systems , vol. 13, no. 4, pp. 517-530, 2005.

[32] Pham D. and Prince J., An Adaptive Fuzzy C- Means Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities, Pattern Recognition Letters , vol. 20, no. 1, pp. 57-68, 1999.

[33] Pham D., Xu C., and Prince J., A Survey of Current Methods in Medical Image Segmentation, Annual Review of Biomedical Engineering , vol. 2, no. 3, pp. 315-337, 2000.

[34] Salman N., Image Segmentation Based on Watershed and Edge Detection Techniques, The International Arab Journal of Information Technology , vol. 3, no. 2, pp. 104-110, 2006.

[35] Sudhavani G. and Sathyaprasad K., Segmentation of Lip Images by Modified Fuzzy C-Means Clustering Algorithm, International Journal of Computer Science and Network Security IJCSNS , vol. 9, no. 4, pp. 187-191, 2009.

[36] Taylor P., Invited Review: Computer Aids for Decision-Making in Diagnostic Radiology - A Literature Review, The British Journal of Radiology , vol. 68, no. 813, pp. 945-957, 1995.

[37] Tolias Y. and Panas S., On Applying Spatial Constraints in Fuzzy Image Clustering Using a Fuzzy Rule Based System, IEEE Signal Processing Letters, vol. 5, no. 10, pp. 245-247, 1998.

[38] Tolias Y. and Panas S., Image Segmentation by a Fuzzy Clustering Algorithm using Adaptive Spatially Constrained Membership Functions, IEEE Transactions on Systems, Man, Cybernetics, vol. 28, no. 3, pp. 359-369, 1998.

[39] Wang Z. and Lu R., A New Algorithm for Image Segmentation Based on Fast Fuzzy C- Means Clustering, in Proceedings of the 2008 International Conference on Computer Science and Software Engineering , USA, pp. 14-17, 2008.

[40] Wells W., LGrimson W., Kikinis R., and Arrdrige S., Adaptive Segmentation of MRI Data, IEEE Transactions on Medical Imaging , vol. 15, no. 4, pp. 429-442, 1996.

[41] Worth A., Makris N., Caviness V., and Kennedy D., Neuroanatomical Segmentation in MRI: Technological Objectives, International Journal of Pattern Recognition and Artificial Intelligence , vol. 11, no. 8, pp. 1161-1187, 1997.

[42] Wu K. and Yang M., Alternative C-Means Clustering Algorithms, Pattern Recognition, vol. 35, no. 10, pp. 2267-2278, 2002.

[43] Yang M. and Wu K., A Similarity-Based Robust Clustering Method, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 26, no. 4, pp. 434-448, 2004.

[44] Yang Y., Zheng C., and Lin P., Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term for Image Segmentation, Opto-Electronics Review , vol. 13, no. 4, pp. 309-315, 2005.

[45] Yu J. and Wang Y., Molecular Image Segmentation Based on Improved Fuzzy Clustering, Journal of Biomedical Imaging , vol. 2007, no. 1687, pp. 1-9, 2008.

[46] Zhang L., Dong W., Zhang D., and Shi G., Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping, Pattern Recognition , vol. 43, pp. 1531-1549, 2010.

[47] Zhou H., Schaefer G., Sadka A., and Celebi E., Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Skin Lesions, in Proceedings A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability 83 of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology , USA, pp. 438-443, 2008.

[48] Zijdenbos A. and Dawant B., Brain Segmentation and White Matter Lesion Detection in MR Images, Critical Reviews in Biomedical Engineering , vol. 22, no. 5-6, pp. 401-465, 1994.

[49] Zulaikha S. and Mohamed M., An Effective Approach for Segmentation of MRI Images: Combining Spatial Information with Fuzzy C- Means Clustering, European Journal of Scientific Research , vol. 41, no. 3, pp. 437-451, 2010. Zulaikha Beevi received her BE, degree from the Department of Civil and Transportation Engineering, Institute of Road and Transport Technology, TamilNadu, India and her M.Tech from the Department of Center for Information Technology and Engineering, Manonmaniam Sundaranar University, TamilNadu, India in 1992 and 2005, respectively. Currently, she is pursuing her PhD, working with Prof. M. Mohamed Sathik and Prof. K. Senthamarai Kannan. She is working as an assistant professor in National College of Engineering, Tirunelveli, TamilNadu, India. Mohamed Sathik received his BSc, and MSc degrees from the Department of Mathematics, and his M.Phill. from the Department of Computer Science, and his M.Tech in computer science and IT, MS, from the Department of Counseling and Psycho Therapy, and his MBA degree from reputed institutions. He has two years working experience as a coordinator for M.Phil Computer Science Program, Directorate of Distance and Continuing Education, M.S. University. He served as an additional coordinator in Indra Gandhi National Open University for four years. He headed the University Study Center for MCA Week End Course, Manonmaniam Sundaranar University for 9 years. He has been with the department of Computer Science, Sadakathullah Appa College for 23 years. Currently, he is working as a reader for the same department. He works in the field of image processing, specializin g particularly in medical imaging. Dr. Mohamed Sathik guided 30 M.Phil Computer Science Scholors and guiding 14 Ph.D Computer Science Scholor from M.S. University, Tirunelveli, Barathiyar University, Coimbatore and Periyar Maniammai University, Tanjavur. He presented 12 papers in international conferences in image processing and presented 10 papers in national conferences. He published 3 pape rs in international journals and 5 papers in proceedin gs with ISBN.