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Seed-Based Region Growing (SBRG) vs Adaptive Network-Based Inference System (ANFIS) vs Fuzzyc-Means (FCM): Brain Abnormalities Segmentation

Authors: Shafaf Ibrahim, Noor Elaiza Abdul Khalid, Mazani Manaf


Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various sizes of abnormalities and pasting it onto normal brain tissues. The normal tissues or the background are divided into three different categories. The segmentation is done with fifty seven data of each category. The knowledge of the size of the abnormalities by the number of pixels are then compared with segmentation results of three techniques proposed. It was proven that the ANFIS returns the best segmentation performances in light abnormalities, whereas the SBRG on the other hand performed well in dark abnormalities segmentation.

Keywords: Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS), Fuzzy c-Means (FCM), Brain segmentation.

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[1] Pal N. R., Pal S. K., A Review on Image Segmentation Techniques, Pattern Recognition 26(9), pp. 1277-1294, 1993.
[2] Nayak H., Amini M. M., Bibalan P. T., Bacon N., Medical Image Segmentation, June 12, 2008.
[3] Schmidt M., Levner I., Greiner R., Segmenting Brain Tumors using Alignment-based Features. In Proceedings of the Fourth International Conference on Machine Learning and Applications, 2008.
[4] Nishimura A., Sawada S., Ushiyama I., Tanegashima A., Nakagawa T., Ikemoto K., Postmortem Diagnosis of Brain Disorders, Anil Aggrawal-s Internet Journal of Forensic Medicine and Toxicology, Vol. 1, No. 2, 2000.
[5] Singh J., Daftary A., Iodinated Contrast Media and Their Adverse Reactions, Journal of Nuclear Medicine Technology, Vol. 36, No. 2, pp. 69-74, Society of Nuclear Medicine, Teleradiology Solutions, Bangalore, India, 2008.
[6] Zizzari A., Udo S., Bernd M., Guenther G., Sebastian S., Detection of Tumour in Digital Images of the Brain, Proceedings of the IASTED International Conference of the Signal Processing, Pattern Recognition and Application, Greece, 2001.
[7] Mancas M., Gosselin B., Macq B., Segmentation Using a Region- Growing Thresholding, Image Processing: Algorithms and Systems IV, In: Proceedings of the SPIE, Vol. 5672, pp. 388-398, 2005.
[8] Dong-yong D., Condon B., Hadley D., Rampling R., Teasdale G., Intracranial Deformation Caused by Brain Tumors: Assessment of 3-D Surface by Magnetic Resonance Imaging, IEEE Transactions on Medical Imaging, Vol. 12, Issue 4, , pp. 693-702, 1993.
[9] Bouix S., Martin-Fernandez M., Ungar L., Nakamura M., Koo M., McCarley R., Shenton M., On Evaluating Brain Tissue Classifiers without a Ground Truth, Neuroimage 07, 447458, 2007.
[10] Masroor M. A., Dzulkifli M., Segmentation of Brain MR Images for Tumour Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model, International Journal of Image Processing, Vol. 2, Issue 1, 2008.
[11] Roerdink J., Meijster A., The Watershed Transform: Definitions, Algorithms and Parallelization Strategies, Fundamenta Informaticae, pp. 187- 228, IOS Press, 2001.
[12] Ibrahim S., Khalid N. E. A., Manaf M., Empirical Study of Brain Segmentation using Particle Swarm Optimization, International Conference on Information Retrieval and Knowledge Management, CAMP10, 2010.
[13] Ganesan R., Radhakrishnan S., Segmentation of Computed Tomography Brain Images using Genetic Algorithm, International Journal of Soft Computing Year 2009, Vol. 4, Issue 4, pp. 157-161, 2009.
[14] Noor N. M., Khalid N. E. A., Hassan R., Ibrahim S., Yassin I. M., Adaptive Neuro-Fuzzy Inference System for Brain Abnormality Segmentation, 2010 IEEE Control and System Graduate Research Colloquium, ICSGRC 2010.
[15] Khalid N. E. A., Ibrahim S., Manaf M., Ngah, U. K., Seed-Based Region Growing Study for Brain Abnormalities Segmentation, International Symposium on Information Technology 2010 (ITSim 2010), 2010.
[16] Dubey R. B., Hanmandlu M., Gupta S. K., Semi-automatic Segmentation of MRI Brain Tumor, ICGST-GVIP Journal, ISSN: 1687-398X, Vol. 9, Issue 4, August 2009.
[17] Wasserthal C., Engel K., Rink K., Brechman A., Automatic Segmentation of the Cortical Grey and White Matter in MRI using Region Growing Approach based on Anatomical Knowledge, Springer Berlin Heidelberg, ISBN978-3-540-78639-9. 2008.
[18] Iftekharuddin K. M., Zheng J., Islam M. A., Lanningham F., Brain Tumor Detection in MRI: Technique and Statistical Validation, Fortieth Asilomar Conference on Signals, Systems and Computers, Oct. 29 2006- Nov, pp. 1983-1987, 2006.
[19] Shen S., Snadham W., Granat M., Sterr A.,MRI Fuzzy Segmentation of Brain Tissue using Neighborhood Attraction with Neural Network Optimization, IEEE Transactions on Information Technology in Biomedicine, Vol. 9, Issue 3, Sept. 2005, pp. 459-467, 2005.
[20] Linguraru M. G., Ballester M. A. G., Ayache N., Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging, International Journal of Computers, Communications and Control, 2007(II): pp. 26-36.
[21] Adams R., Bischof L., Seeded Region Growing, IEEE Trans. Pattern Anal. Machine Intell., Vol. 16, pp. 641-647, 1994.
[22] Ngah U. K., Ooi T. H., Sulaiman S. N., Venkatachalam, P. A., Embedded Enhancement Image Processing Techniques on A Demarcated Seed Based Grown Region. Proceedings of Kuala Lumpur International Conference on Biomedical Engineering, pp. 170-172, 2002.
[23] Venkatachalam P. A., Ngah U. K., Hani A. F. M., Shakaff A. Y. M.,Seed Based Region Growing Technique in Breast Cancer Detection and Embedded Expert System, Proceedings of International Conference on Artificial Intelligence in Engineering and Technology, pp. 464-469, 2002.
[24] Dubey R. B., Hanmandlu M., Gupta S. K., Semi-automatic Segmentation of MRI Brain Tumor, ICGST-GVIP Journal, ISSN: 1687-398X, Vol. 9, Issue 4, August 2009, 2009.
[25] Ooi T. H., Ngah U. K., Khalid N. E. A., Venkatachalam P. A., Mammographic Calcification Clusters Using The Region Growing Technique. Proceedings of the New Millennium International Conference on Pattern Recognition, Image, 2000.
[26] Mancas M., Gosselin B., Macq B., Segmentation using a Region- Growing Thresholding, Proceedings of the SPIE, Image Processing: Algorithms and Systems IV, Vol. 5672, pp. 388-398, 2005.
[27] Yen J., Langari R., Fuzzy logic: Intelligence, Control and Information. Prentice-Hall Inc., pp. 444, 1999.
[28] Fuzzy Logic Toolbox, For use with Matlab, users guide, Version 2, pp. 220, The MathWorks, Inc., 2005.
[29] Yu S., Guan L., A CAD System for the Automatic Detection of Clustered Microcalcification in Digitized Mammogram Films, IEEE Transactions on Medical Imaging, 19, pp. 115-126, 2000.
[30] Belal S. Y., Taktak A. F. G., Nevill A. J., Spencer S. A., Roden D., Bevan S., Automatic Detection of Distorted Plethysmogram Pulses in Neonates and Pediatric Patients using an Adaptive-Network Based Fuzzy Inference System. Artificial Intelligence in Medicine, 24, pp. 149-165, 2002.
[31] Ubeyli E. D., Guler I., Automatic Detection of Erythematosquamous Diseases using Adaptive Neuro-Fuzzy Inference Systems, Computers in Biology and Medicine, 35, pp. 421-433, 2005.
[32] Ubeyli E. D., Guler I., Adaptive Neuro-Fuzzy Inference Systems for Analysis of Internal Carotid Arterial Doppler Signals, Comput Biol Med, 2005, in press.
[33] Stylios C. D., Groumpos P. P., Fuzzy Cognitive Maps in Modeling Supervisory Control Systems, Journal of Intelligent Fuzzy System 8, pp. 83-98, 2000.
[34] Miao Y., Liu Z., On Causal Inference in Fuzzy Cognitive Maps, IEEE Trans. Fuzzy Syst. 8, pp. 107-119, 2000.
[35] Liu Z., Satur R., Contextual Fuzzy Cognitive Map for Decision Support In Geographic Information Systems, IEEE Trans. Fuzzy Syst. 5, pp. 495- 507, 1999.
[36] Chang X., Li W., Farrell J., A C-means Clustering Based Fuzzy Modeling Method, The Ninth IEEE International Conference on Fuzzy Systems, Vol.2, pp. 937-940, BIME Journal, Vol. 06, Issue 1, Dec. 2006.
[37] Kannan S. R., Segmentation of MRI using New Unsupervised Fuzzy C Mean Algorithm, ICGST International Journal on Graphics, Vision and Image Processing, Vol.05, No.2, pp.17-23, January, 2005.
[38] Murugavalli S. Rajamani V., A High Speed Parallel Fuzzy C-Mean Algorithm for Brain Tumor Segmentation, BIME Journal, Volume 6, Issue 1, pp. 29-34, Dec., 2006.
[39] Papageorgiou E.I., Stylios C.D., Groumpos P.P., An Integrated Two- Level Hierarchical Decision Making System Based on Fuzzy Cognitive Maps, IEEE Trans. Biomed. Eng. 50 (12), pp. 1326-1339, 2003.
[40] Liew AWC, Yan H., An Adaptive Spatial Fuzzy Clustering Algorithm for MR Image Segmentation, IEEE Trans. Med. Imag. 2003; 22(9): pp. 1063-75.
[41] Papageorgiou E.I., Spyridonos P. Ravazoula P., Stylios C. D., Groumpos P. P., Nikiforidis G., Advanced Soft Computing Diagnosis Method for Tumor Grading, Artif. Intell. Med. 36 (1), pp. 59-70, 2006.
[42] Chuang K. S., Tzeng H. L., Chen S., Wu J., Chen T. J,. Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics, 30: pp. 9-15, 2006.
[43] Spyridonos P., Papageorgiou E. I., Groumpos P. P., Nikiforidis G., Integration of Expert Systems with Image Analysis Techniques for Medical Diagnosis, In: Proc ICIAR 2006, Lecture Notes in Computer Science, Vol. 4142, Springer-Verlag, pp. 110-121, 2006.
[44] Gribskov M., Robinson N. L., Use of Receiver Operating Characteristic (ROC) Analysis to Evaluate Sequence Matching Comput. Chem., Vol. 20(1), pp. 25-33, 1996.
[45] Qian W., Li L., Clarke L. P., Image Feature Extraction for Mass Detection In Digital Mammography: Influence of Wavelet Analysis, The International Journal of Medical Physics Research and Practice, Vol. 26, Issue 3, 1999.
[46] Joao V. B. S., Jorge J. G. L., Roberto M. C. Jr., Herbert F. J., Michael J. C., Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification, Journal of IEEE Trans Medical Imaging, Vol. 25, no. 9, pp. 1214-1222, Sep. 2006.
[47] Budde M. D., Kim J. H., Liang H. F., Schmidt R. E., Russell J. H., Cross A. H., Song S. K., Toward Accurate Diagnosis of White Matter Pathology using Diffusion Tensor Imaging, Journal of Magnetic Resonance in Medicine, Vol. 57, Issue 4, pp. 688695, April 2007.
[48] Sigal L., Sclaroff S., Athitsos V., Skin Color-Based Video Segmentation under Time-Varying Illumination, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, Issue 7, pp. 862-877, July 2004.
[49] Trochim W., The Research Methods Knowledge Base, 2nd Edition. Atomic Dog Publishing, 2000, Cincinnati, OH.
[50] Kayvan N., Robert S., Biomedical Signal and Image Processing, Published by CRC Press, ISBN 0849320992, 2006.
[51] Fan S. L. X., Man Z., Samur R., Edge Based Region Growing - A New Image Segmentation Method, Proceedings of the ACM SIGGRAPH International Conference on Virtual Reality Continuum and its Applications in Industry, pp. 302-305, ISBN:1-58113-884-9, 2004.
[52] Le-Mair, M. W., Reeves, A. P., Region Growing on a Hypercube Multiprocessor, Proceedings of the Third Conference on Hypercube Concurrent Computers and Applications, Vol. 2, Pasadena, California, United States, pp. 1033-1042, ISBN:0-89791-278-0, 1989.
[53] Hai O. T., Ngah U. K., Khalid N. E. A., Venkatachalam P. A., Region Growing Techniques on Breast Ultrasound Images, Proceedings of the New Millennium International Conference on Pattern Recognition, Image Processing and Robot Vision (PRIPROV), 2000.
[54] Mat-Isa N. A., Mashor M. Y., Othman N. H., Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer, International Journal of The Computer, the Internet and Management, Vol. 13(1), pp 61-70, January-April 2005.
[55] Jang J. S. R., Sun C. T., Mizutani E., Neurofuzzy and soft computing. A Computational Approach to Learning and Machine Intelligent. United States of America. Prentice Hall International, 1997
[56] Nauck D., Klawonn F., Kruse R., Foundations of Neuro-Fuzzy Systems. England. John Wiley & Sons Ltd, 1997.
[57] Albayrak S., Fatih Amasyal F., Fuzzy c-Means Clustering on Medical Diagnostic Systems, International XII. Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN), 2003.