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IJSTR >> Volume 8 - Issue 7, July 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Novel Center Symmetric Local Binary Pattern And Chi Square Fuzzy C-Mean Clustering Based Segmentation In Medical Imaging Technique

[Full Text]



G. Anand Kumar, P. V. Sridevi



image clustering, brain tumor segmentation, fuzzy algorithm, threshold, magnetic resonance imaging



Accurate brain tumor segmentation is a challenging task from the Magnetic Resonance Imaging (MRI) in the field of medical image processing. For this purpose, we propose a Novel Center Symmetric-Local Binary Pattern (CS-LBP) and Chi Square Fuzzy C-mean based segmentation via clustering to segment the abnormal tissues from the normal region. Initially, preprocessing is performed to extricate the region of interest based on improved threshold and center symmetric LBP. Then we compare the preprocessing output and original MRI image using Bhattacharya similarity metrics to obtain the region of interest from the imaging technology. Finally, Chi Square distance based Fuzzy C-Mean (CS-FCM) segmentation is performed to cluster the region according to the feature based on region of interest (ROI), including entropy, contrast, and mean for necrosis, edema and enhanced tumor regions. BRATS 2015 dataset is used to evaluate the performance in terms of Jaccard matching, specificity, Positive Predictive Value (PPV) and Dice Similarity Coefficient (DSC). The existing approaches are not efficient and predictive whereas our proposed method performs better in clustering the tumor into three regions (necrosis, edema and enhanced tumor) based on the region of interest.



[1] Kaya I.E, Pehlivanlı A.Ç, Sekizkardeş E.G and Ibrikci T, PCA based clustering for brain tumor segmentation of T1w MRI images. Computer methods and programs in biomedicine. 1;140:19-28. 2017.
[2] Khaloo A and Lattanzi D. “Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models”. Advanced Engineering Informatics. 1;34: 1-6. 2017.
[3] Pare S, Bhandari A.K, Kumar A and Singh G.K. “A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm”. Computers & Electrical Engineering. 1;70:476-95. 2018.
[4] Raith S, Vogel E.P, Anees N, Keul C, Güth JF, Edelhoff D and Fischer H. “Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data”. Computers in biology and medicine. 80: 65-76. 2017.
[5] Song J.H, Cong W and Li J. “A Fuzzy C-means Clustering Algorithm for Image Segmentation Using Nonlinear Weighted Local Information”. Journal of Information Hiding and Multimedia Signal Processing. 8(9):1-1. 2017.
[6] Zanaty E.A. “Determination of gray matter (GM) and white matter (WM) volume in brain magnetic resonance images (MRI)”. International Journal of Computer Applications. 45(3):16-22. 2012.
[7] Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P.M, and Larochelle H. “Brain tumor segmentation with deep neural networks”. Medical image analysis. 2017.1;35:18-31.
[8] Pezoulas V.C, Zervakis M, Pologiorgi I, Seferlis S, Tsalikis G.M, Zarifis G and Giakos G.C. “A tissue classification approach for brain tumor segmentation using MRI. InImaging Systems and Techniques (IST)”, 2017 IEEE International Conference (pp. 1-6). IEEE.
[9] Salve, M.V.P., Salve, M.A.K. and Jondhale, M.K., “Brain Tumor Segmentation Using MS Algorithm”. Brain. 2017.
[10] Mirzaei, G. and Adeli, H. “Segmentation and clustering in brain MRI imaging”. Reviews in the Neurosciences, 30(1), pp.31-44. 2018.
[11] Agrawal R, Sharma M and Singh B.K. “Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology”. Journal of The Institution of Engineers (India): Series B. 2018. 1;99(2):173-80.
[12] Ganesh M, Naresh M and Arvind C. “MRI brain image segmentation using enhanced adaptive fuzzy k-means algorithm”. Intelligent Automation & Soft Computing. 2017 Apr 3;23(2):325-30.
[13] Kumar, R. and Mathai, K.J. “Brain tumor segmentation by modified K-mean with morphological operations”. Int J Innov Res Sci Eng Technol, 6(8). 2017.
[14] Mane D.S Gite B.B. “Brain Tumor Segmentation Using Fuzzy C-Means and K-Means Clustering and Its Area Calculation and Disease Prediction Using Naive-Bayes Algorithm”. Brain. 6(11).
[15] Zhao X, Wu Y, Song G, Li Z, Zhang Y and Fan Y. “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical image analysis. 2018. 1;43:98-111.
[16] Ding Y, Dong R, Lan T, Li X, Shen G, Chen H and Qin Z. “Multi‐modal brain tumor image segmentation based on SDAE”. International Journal of Imaging Systems and Technology. 2018. 28(1):38-47.
[17] Shivhare S.N, Sharma S and Singh N “An Efficient Brain Tumor Detection and Segmentation in MRI Using Parameter-Free Clustering”.. InMachine Intelligence and Signal Analysis, pp. 485-495. 2019. Springer, Singapore.
[18] Kumar G.A and Sridevi P.V. “3D Deep Learning for Automatic Brain MR Tumor Segmentation with T-Spline Intensity Inhomogeneity Correction”. Automatic Control and Computer Sciences. 2018. 1;52(5):439-50.
[19] Bal A, Banerjee M, Sharma P and Maitra M. “Brain Tumor Segmentation on MR Image Using K-Means and Fuzzy-Possibilistic Clustering”. In2018 2nd International Conference on Electronics, Materials Engineering & Nano-Technology, (IEMENTech). pp. 1-8. 2018. IEEE.
[20] Shen G, Ding Y, Lan T, Chen H and Qin Z. “Brain Tumor Segmentation Using Concurrent Fully Convolutional Networks and Conditional Random Fields”. In Proceedings of the 3rd International Conference on Multimedia and Image Processing, pp. 24-30. 2018 ACM.