Brain Tumor Recognition from MRI Images using Multiple OTSU Thresholding and Cross Correlation

Ankita Kapil, Jabalpur Engineering College, Jabalpur Mp India; Dr. Shailja Shukla ,Jabalpur Engineering College, Jabalpur Mp India

Graphical User Interfacing (GUI), Image processing, brain tumor, Image segmentation, Magnetic resonance imaging (MRI)

Medical image processing is the most challenging and emerging field now a day. Processing of MRI images is one of the parts of this field. This paper work describes a good and efficient strategy to detect and extract the brain tumor[4][5] from patient’s brain MRI images. In this method an algorithm is developed, the primary part of this algorithm is to convert given MRI image into binary image (0 and 1) using segmentation technique[6] called multiple Otsu Thresholding[7], after which morphological operations[8] [9]has been applied. The obtained image is matched with the SRI24 database which is a MRI-based atlas of 24 normal adult human brain anatomies. The work is done and comprised by making a graphical user interface (GUI) in MATLAB. The algorithm results in good accuracy and better time for recognition of the brain tumor.
    [1] Mrs. Sara Sandabad et al, Mohamed V University Rabat, Morocco represented a work title “New tumor detection procedure using Nl-means filter & histogram study” In IEEE international Conference which is been accepted for IEEE Explore digital Library held in 2015. [2] Mr. Stefan Bauer et al a Student Member of IEEE, represented a work title “Multi-scale Modeling for Image study of Brain Tumor Studies” in IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING published at VOL. 59, NO. 1 journal published in 2012 [3] Rajesh C. Patil, Dr. A. S. Bhalchandra, Brain Tumour Extraction from MRI Images Using MATLAB,International Journal of Electronics, Communication & Soft Computing Science and Engineering ISSN: 2277-9477, Volume 2, Issue 11 [4] Zülch, K. J. "Principles of the new World Health Organization (WHO) classification of brain tumors." Neuroradiology 19.2 (1980): 59-66 [5] Bushberg, Jerrold T., and John M. Boone. The essential physics of medical imaging. Lippincott Williams & Wilkins, 2011. [6] Liang, Zhengrong. "Tissue classification and segmentation of MR images." IEEE Engineering in Medicine and Biology Magazine 12.1 (1993): 81-85. [7] KalavathiP,Brain Tissue Segmentation in MR Brain Images using Multiple Otsu's Thresholding Technique,the 8th International Conference on Computer Science & Education (ICCSE 2013), April 26-28, 2013. Colombo, Sri Lanka, SuD1.3,978-1-4673-4463-0/13, 2013 IEEE, pp 639-645 [8] F. G. B. De Natale and G. Boato, "Detecting Morphological Filtering of Binary Images," in IEEE Transactions on Information Forensics and Security, vol. 12, no.5,pp.1207-1217,May2017.doi: 10.1109/TIFS.2017.2656472. [9] N. Jamil, T. M. T. Sembok and Z. A. Bakar, "Noise removal and enhancement of binary images using morphological operations," 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008, pp. 1-6.doi: 10.1109/ITSIM.2008.4631954 [10] Bezdek, James C., L. O. Hall, and L_P Clarke. "Review of MR image segmentation techniques using pattern recognition." Medical physics 20.4 (1993): 1033-1048 [11] Liang, Zhengrong. "Tissue classification and segmentation of MR images." IEEE Engineering in Medicine and Biology Magazine 12.1 (1993): 81-85. [12] Hall, Lawrence O., et al. "A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain." IEEE transactions on neural networks 3.5 (1992): 672-682. [13] Natarajan, Prem, et al. "Tumor detection using threshold operation in MRI brain images." Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on. IEEE, 2012 [14] Zabir, Ishmam, et al. "Automatic brain tumor detection and segmentation from multi-modal MRI images based on region growing and level set evolution." Electrical and Computer Engineering (WIECON-ECE), 2015 IEEE International WIE Conference on. IEEE, 2015 [15] I. Maiti and M. Chakraborty, "A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model," 2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS, Durgapur, 2012, pp.1-5. doi: 10.1109/NCCCS.2012.6413020 [16] I. Diaz and P. Boulanger, "Atlas to patient registration with brain tumor based on a mesh-free method," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 2924-2927.doi: 10.1109/EMBC.2015.7319004 [17] D. M. Joshi, N. K. Rana and V. M. Misra, "Classification of Brain Cancer using Artificial Neural Network," 2010 2nd International Conference on Electronic Computer Technology, Kuala Lumpur,2010,pp.112-116.doi: 10.1109/ICECTECH.2010.5479975 [18] V. Kumar, J. Sachdeva, I. Gupta, N. Khandelwal and C. K. Ahuja, "Classification of brain tumors using PCA-ANN," 2011 World Congress on Information and Communication Technologies, Mumbai,2011,pp.1079-1083.doi: 10.1109/WICT.2011.6141398 [19] Pan Lin, Yong Yang and Chong-XunZheng, "An efficient brain magnetic resonance image segmentation method," Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2004, pp. 2757-2760 vol.5. doi: 10.1109/ICMLC.2004.1378499 [20] H. S. Abdulbaqi, MohdZubir Mat, A. F. Omar, I. S. B. Mustafa and L. K. Abood, "Detecting brain tumor in Magnetic Resonance Images using Hidden Markov Random Fields and Threshold techniques," 2014 IEEE Student Conference on Research and Development,BatuFerringhi, 2014, pp. 1-5.doi: 10.1109/SCORED.2014.7072963 [21] T. Rohlfing, N. M. Zahr, E. V. Sullivan, & A. Pfefferbaum, “The SRI24 multichannel atlas of normal adult human brain structure,” Human Brain Mapp., vol. 31, no. 5, pp. 798–819, May 2010. [22] Dechevsky L.T., Gundersen J., Grip N. (2010) Wavelet Compression, Data Fitting and Approximation Based on Adaptive Composition of Lorentz-Type Thresholding and Besov-Type Non-threshold Shrinkage. In: Lirkov I., Margenov S., Waśniewski J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg [23] Wang, Shu-Lin, et al. "Robust classification method of tumor subtype by using correlation filters." IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 9.2 (2012): 580-591. [24] Sakkatos, Promprawatt, et al. "Analysis of text-based CAPTCHA images using Template Matching Correlation technique." Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), 2014 4th Joint International Conference on. IEEE, 2014.
Paper ID: GRDJEV02I050005
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 5 - 13