%0 Journal Article %1 noauthororeditor %A Maolood, %A Lu, %A Al-Salhi, %A resheedi, %A Ince, %D 2018 %J IJIRIS:: International Journal of Innovative Research in Information Security %K cerebral fuzzy mri segmentation tissue %N Issue V %P 25-35 %R doi://10.26562/IJIRIS.2018.JYIS10080 %T FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM %U http://www.ijiris.com/volumes/Vol5/iss5/01.JYIS10080.pdf %V Volume V %X The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.

@article{noauthororeditor, abstract = {The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.}, added-at = {2018-08-11T16:05:05.000+0200}, author = {Maolood and Lu and Al-Salhi and resheedi and Ince}, biburl = {https://www.bibsonomy.org/bibtex/20d844e3901cc67f9c221b1ad2e689d50/ijiris}, description = {Image segmentation is an essential technique for processing MRI cerebral tissue images. Image segmentation is the separation of a digital image into numerous segments or pixel sets, in which pixels are grouped similarly according to homogeneous criteria, such as texture, intensity, or color, to discover items and borders inside the defined image [1]. Many algorithms, such as graph cut, level set, edge detection, and clustering, have been proposed for medical image segmentation [2]. Medical image segmentation aims to divide images into homogeneous partitions that concern other pixel neighborhoods to make these images significant in realizing the aims of medical images. The results of image segmentation are processed by extracting a set of segments, regions, or contours of the image. Pixels in a region possess several similar characteristics or computed properties, such as contrast, texture, color, and grayscale [3]. Fuzzy c-means (FCM) is an important algorithm used in MRI cerebral tissue segmentation [4, 5]. FCM is successfully improving the performance of medical image segmentation [5]. Jiang et al. [2] are introduced and proposed the current scheme with spatial constraints LCFCM_s algorithm for spatial information to the FCM algorithm which pixel enables to affect by its immediate neighborhood. This method entails increased time consumption because of the calculation in each iteration step to the spatial neighborhood [2].}, doi = {doi://10.26562/IJIRIS.2018.JYIS10080}, interhash = {32e0fa195e487aa50546481a5cfd9e9a}, intrahash = {0d844e3901cc67f9c221b1ad2e689d50}, issn = {2349-7017}, journal = {IJIRIS:: International Journal of Innovative Research in Information Security}, keywords = {cerebral fuzzy mri segmentation tissue}, language = {English}, month = {July 2018}, note = { [1] J. Umamaheswari and G. Radhamani, “A fusion technique for medical image segmentation,”in Devices, Circuits and Systems (ICDCS), 2012 International Conference on, 653–657, IEEE (2012). [2] X.-L. Jiang, Q. Wang, B. He, et al., “Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints,” Neurocomputing 207, 22–35 (2016). [3] M. Sato-Ilic, Innovations in fuzzy clustering: Theory and applications, vol. 205, Springer Science & Business Media (2006). [4] J. K. Udupa and S. Samarasekera, “Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation,” Graphical models and image processing 58(3), 246–261 (1996). [5] W. Cai, S. Chen, and D. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern recognition 40(3), 825–838 (2007). [6] F. Z. Benchara, M. Youssfi, O. Bouattane, et al., “A new scalable, distributed, fuzzy cmeans algorithm-based mobile agents scheme for hpc: Spmd application,” Computers 5(3), 14 (2016). [7] G. Ilango and R. Marudhachalam, “New hybrid filtering techniques for removal of Gaussian noise from medical images,” ARPN Journal of Engineering and Applied Sciences 6(2), 8–12 (2011). [8] M. Habib, A. Hussain, S. Rasheed, et al., “Adaptive fuzzy inference system based directional median filter for impulse noise removal,” AEU-International Journal of Electronics and Communications 70(5), 689–697 (2016). [9] A. Diaz-Sanchez, J. Lemus-Lopez, J. M. Rocha Perez, et al., “Ultra low-power analog median filters.,” Radioengineering 22(3) (2013). [10] A. Makandar and B. Halalli, “Image enhancement techniques using highpass and lowpass filters,” International Journal of Computer Applications 109(14) (2015). [11] X. Kang, M. C. Stamm, A. Peng, et al., “Robust median filtering forensics based on the autoregressive model of median filtered residual,” in Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific, 1–9, IEEE (2012). [12] F. Khateb, T. Kulej, and M. Kumngern, “0.5-v dtmos median filter,” AEU-International Journal of Electronics and Communications 69(11), 1733–1736 (2015). [13] A. Norouzi, M. S. M. Rahim, A. Altameem, et al., “Medical image segmentation methods, algorithms, and applications,” IETE Technical Review 31(3), 199–213 (2014). [14] S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations,” Journal of computational physics 79(1), 12–49 (1988). [15] C. Li, C.-Y. Kao, J. C. Gore, et al., “Minimization of region-scalable fitting energy for image segmentation,” IEEE transactions on image processing: a publication of the IEEE Signal Processing Society 17(10), 1940 (2008). [16] L. Wang and C. Pan, “Robust level set image segmentation via a local correntropy-based k-means clustering,” Pattern Recognition 47(5), 1917–1925 (2014). [17] L. Khelifi and M. Mignotte, “Efa-bmfm: A multi-criteria framework for the fusion of colour image segmentation,” Information Fusion 38, 104–121 (2017). [18] Y.-T. Chen, “A novel approach to segmentation and measurement of medical image using level set methods,” Magnetic resonance imaging 39, 175–193 (2017). [19] M. Sadaaki, I. Hidetomo, and H. Katsuhiro, “Algorithms for fuzzy clustering: methods in c-means clustering with applications,” German: Springer (2008). [20] S. Krinidis and V. Chatzis, “A robust fuzzy local information c-means clustering algorithm,” IEEE transactions on image processing 19(5), 1328–1337 (2010). [21] S. S. Kumar, R. S. Moni, and J. Rajeesh, “Automatic segmentation of liver tumour using a possibilistic alternative fuzzy c-means clustering,” International Journal of Computers and Applications 35(1), 6–12 (2013). [22] H. Shamsi and H. Seyedarabi, “A modified fuzzy c-means clustering with spatial information for image segmentation,” International Journal of Computer Theory and Engineering 4(5), 762 (2012). [23] X. Yang, S. Zhan, D. Xie, et al., “Hierarchical prostate MRI segmentation via level set clustering with shape prior,” Neurocomputing 257, 154–163 (2017). [24] Y.-F. Tsai, I.-J. Chiang, Y.-C. Lee, et al., “Automatic mri meningioma segmentation using estimation maximization,” in Engineering in Medicine and Biology Society, 2005. IEEEEMBS 2005. 27th Annual International Conference of the, 3074–3077, IEEE (2005). [25] V. Bhateja, K. Rastogi, A. Verma, et al., “A non-iterative adaptive median filter for image denoising,” in Signal Processing and Integrated Networks (SPIN), 2014 International Conference on, 113–118, IEEE (2014). [26] A. Kaur, R. Malhotra, and R. Kaur, “Performance evaluation of non-iterative adaptive median filter,” in Advance Computing Conference (IACC), 2015 IEEE International, 1117–1121, IEEE (2015). [27] T. Altameem, E. Zanaty, and A. Tolba, “A new fuzzy c-means method for magnetic resonance image brain segmentation,” Connection Science 27(4), 305–321 (2015). [28] Y. Ding and X. Fu, “Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm,” Neurocomputing 188, 233–238 (2016). [29] M. Balafar, A. Ramli, S. Mashohor, et al., “Compare different spatial based fuzzy-c mean (fcm) extensions for mri image segmentation,” in Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on, 5, 609–611, IEEE (2010). [30] D. J. Hemanth, J. Anitha, and V. E. Balas, “Performance improved modified fuzzy c-means algorithm for image segmentation applications,” Informatica 26(4), 635–648 (2015). [31] C. Nath, J. Talukdar, and P. Talukdar, “Robust fuzzy c-mean algorithm for segmentation and analysis of cytological images,” International Journal 1(1) (2012). [32] S. Yazdani, R. Yusof, A. Karimian, et al., “Image segmentation methods and applications in mri brain images,” IETE Technical Review 32(6), 413–427 (2015). [33] R. Suganya and R. Shanthi, “Fuzzy c-means algorithm-a review,” International Journal of Scientific and Research Publications 2(11), 1 (2012). [34] T. Friedrich, T. K¨otzing, M. S. Krejca, et al., “The compact genetic algorithm is efficient under extreme gaussian noise,” IEEE Transactions on Evolutionary Computation 21(3), 477–490 (2017). [35] R. B. Ali, R. Ejbali, and M. Zaied, “Gpu-based segmentation of dental x-ray images using active contours without edges,” in Intelligent Systems Design and Applications (ISDA), 2015, 15th International Conference on, 505–510, IEEE (2015). [36] Y. Chen, H. D. Tagare, S. Thiruvenkadam, et al., “Using prior shapes in geometric active contours in a variational framework,” International Journal of Computer Vision 50(3), 315–328 (2002).}, number = {Issue V}, pages = {25-35}, timestamp = {2018-08-11T16:05:05.000+0200}, title = {FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM}, url = {http://www.ijiris.com/volumes/Vol5/iss5/01.JYIS10080.pdf}, volume = {Volume V}, year = 2018 }