Abstract
Image segmentation is a vital step in many imaging applications, such as medical images and computer vision. Image segmentation is considered as a challenging problem, so we need to develop an efficient, fast technique for medical image segmentation. In this paper, we propose a new system for a multi-resolution MRI brain image segmentation, which is based on a morphological pyramid with fuzzy C-mean (FCM) clustering. In the first stage, we use a wavelet multi-resolution to maintain spatial context between pixels. Secondly, we use the morphological pyramid to fuse the resulting multi-resolution images with the original image to increase sharpness and decrease noise in the processed image. Finally, we use FCM technique to segment the processed images. We compared our proposed system with some state of the art segmentation techniques on two different brain data sets. Experimental results showed that the proposed system improves the accuracy of the MRI brain image segmentation.
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References
Begum S.A., Devi O.M.: Fuzzy algorithms for pattern recognition in medical diagnosis. Assam Univ. J. Sci. Technol. 7(2), 1–12 (2011)
Withey, D.J.; Koles, Z.J.: Medical image segmentation: methods and software. In: The Proceedings of Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging (NFSI&ICFBI), pp. 140–143 (2007)
Hou Z.: A review on MR image intensity in homogeneity correction. Int. J. Biomed. Imaging 2006, 1–11 (2006)
Ghamisi P., Couceiro M.S., Benediktsson J.A., Ferreira N.M.F.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39, 12407–12417 (2012)
Abdel Maksoud, E.; Elmogy, M.; Al-Awadi, R.M.: Efficient Hybrid Clustering Techniques for Brain Magnetic Resonance Image Segmentation. In: The Proceedings of the 2nd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA14), Cairo, Egypt, Nov. 2014
Madhulatha T.S.: An overview on clustering methods. Int. Organ. Sci. Res. J. Eng. 2(4), 719–725 (2012)
Acharya1, J., Gadhiya2, S., Raviya, K.: Segmentation techniques for image analysis: a review. Int. J. Comput. Sci. Manag. Res. 2(1), 1218–1221 (2013)
Ajala A.F., Oke A.O., Alade M.O., Adewusi A.E.: Fuzzy K–C-means clustering algorithm for medical image segmentation. J. Inf. Eng. Appl. 2(6), 21–32 (2012)
Zanaty E.A.: Improved region growing method for magnetic resonance images (MRIs) segmentation. Am. J. Rem. Sens. 1, 53–60 (2013)
Raju P.D.R., Neelima G.: Image Segmentation by using histogram thresholding. Int. J. Comput. Sci. Eng. Technol. 2(1), 776–779 (2012)
Kaur N., Kaur Sahiwal J., Kaur N.: Efficient K-means clustering algorithm using ranking method in data mining. Int. J. Adv. Res. Comput. Eng. Technol. 1, 85–91 (2012)
Acharya J., Gadhiya S., Raviya K.: Segmentation techniques for image analysis: a review. Int. J. Comput. Sci. Manag. Res. 2(1), 1218–1221 (2013)
Bora1, D.J., Gupta, A.K.: Clustering approach towards image segmentation: an analytical study. Int. J. Res. Comput. Appl. Robot. 2(7), 115–124 (2014)
Kim J., Kim H.: Multiresolution-based watersheds for efficient image segmentation. Pattern Recognit. Lett. 24, 473–488 (2003)
Priya M., Gobu C.: A wavelet-based method for text segmentation in color images. Int. J. Comput. Appl. 69(3), 14–17 (2013)
Mostfa, M.G.; Tolba, M.F.: Medical image segmentation using a wavelet-based multiresolution EM algorithm. In: IEEE International Conference on Industrial Electronics, Technology & Automation (2001)
Chuang K.S., Tzeng H.L., Chen S., Wu J., Chen T.J.: Fuzzy C-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30, 9–15 (2006)
Kaiser G.: The fast Haar transform: gateway to wavelet. IEEE Potent. Mag. 17, 34–37 (1998)
Bedi S.S., Khandelwal R.: Comprehensive and comparative study of image fusion techniques. Int. J. Soft Comput. Eng. 3(1), 2231–2307 (2013)
Singh R., Khare, A.: Multiscale medical image fusion in wavelet domain. Sci. World J. 2013, 1–10 (2013).
Dunn J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32–57 (1974)
Bezdek J.C.: A convergence theorem for the fuzzy data clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2, 1–8 (1980)
Bezdek J.C., Hall L.O., Clarke L.P.: Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20, 1033–1048 (1993)
Yang Y.: Image segmentation by fuzzy C-means clustering algorithm with a novel penalty term. Comput. Inform. 26, 17–31 (2007)
Ramathilagam S., Pandiyarajan R., Sathy A.: Modified fuzzy C-means algorithm for segmentation of T1–T2-weighted brain MR. J. Comput. Appl. Math. 235, 1578–1586 (2011)
Khalifa I., Youssif A., Youssry H.: MRI brain image segmentation based on wavelet and FCM algorithm. Int. J. Comput. Appl. 47(16), 32–39 (2012)
Bandhyopadhyay S., Paul T.: Automatic segmentation of brain tumour from multiple images of brain MRI. Int. J. Appl. Innov. Eng. Manag. 2(1), 240–248 (2013)
Parvathi K., Rao B.S.P., Das M.M., Rao T.V.: Pyramidal watershed segmentation algorithm for high-resolution remote sensing images using discrete wavelet transforms. Discrete Dyn. Nat. Soc. 2009, 1–11 (2009)
Arakeri M.P., Reddy G.R.M.: Efficient fuzzy clustering based approach to brain tumor segmentation on MR images. Commun. Comput. Inf. Sci. 250, 790–795 (2011)
Javed A., Chai W.Y., Thuramaiyer N.K., Javed M.S., Alene A.R.: Automated segmentation of brain MR images by combining contourlet transform and K-Means clustering techniques. J. Theor. Appl. Inf. Technol. 54, 82–91 (2013)
AlZubi S., Islam N., Abbod M.: Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation. Int. J. Biomed. Imaging 2011, 1–18 (2011)
Uniyal N., Verma S.K.: Image fusion using morphological pyramid consistency method. Int. J. Comput. Appl. 95(25), 34–38 (2014)
Simulated Brain Database, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University: http://www.bic.mni.mcgill.ca/brainweb. Accessed 27 Jun 2014
MICCA nice 2012: http://www2.imm.dtu.dk/projects/BRATS2012/data.html. Accessed 9 Aug 2014
Kannan S.R., Sathya A., Ramathilagam S., Devi R.: Novel segmentation algorithm in segmenting medical images. J. Syst. Softw. 83, 2487–2495 (2010)
He R., Datta S., Sajja B.R., Narayana P.A.: Generalized fuzzy clustering for segmentation of multispectral magnetic resonance images. Comput. Med. Imaging Graph. 32, 353–366 (2008)
FMRIB FSL Software Library: http://www.fmrib.ox.ac.uk/fsl/. Accessed 30 Dec 2011)
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Ali, H., Elmogy, M., El-Daydamony, E. et al. Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering. Arab J Sci Eng 40, 3173–3185 (2015). https://doi.org/10.1007/s13369-015-1791-x
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DOI: https://doi.org/10.1007/s13369-015-1791-x