An Efficient Hybrid Segmentation Algorithm for Computer Tomography Image Segmentation
Medical Image segmentation plays a major role in medical image processing. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than the other. The proposed method utilizes clustering with distance based segmentation approach for Computer tomography image segmentation. This paper provides new hybrid segmentation method based on K-Means, Medoid shift and Signature Quadratic Form Distance algorithm for computer tomography images. We validate the Hybrid segmentation approach with the parameters in terms of sensitivity, specificity, accuracy and number of fragments. The Real time dataset is used to evaluate the performance of the proposed method. The results obtained from the experimentation show that the proposed approach attains reliable segmentation accuracy and also clear that it is more efficient, robust and more appropriate for organ classification.
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Dinesh D. Patil, Sonal G. Deore, Medical Image Segmentation: A Review, International Journal of Computer Science and Mobile Computing, Vol. 2, n.1, pp.22 – 27, 2013.
Neeraj Sharma and Lalit M. Aggarwal, Automated medical image segmentation technique. Journal of Medical Physics, Vol.35, n.1, pp.3–14, 2010.
Ladak HM, Mao F, Wang Y, Downey DB, Steinman DA, Fenster A., Prostate boundary segmentation from 2D ultrasound images, Journal of Medical Physics, Vol.27,n.8,pp.1777-1788,2000.
Yiqiang Zhan and Dinggang Shen, Deformable Segmentation of 3-D Ultrasound Prostate Images Using Statistical Texture Matching Method, IEEE Transactions on Medical Imaging, Vol. 25, n. 3, pp.256-272, 2006.
Djamal Boukerroui , Atilla Baskurt c, J. Alison Noble , Olivier Basset , Segmentation of ultrasound images––multiresolution 2D and 3D algorithm based on global and local statistics, Pattern Recognition Letters,Vol.24 , pp.779–790, 2003.
Ashish Thakur Radhey Shyam Anand, A Local Statistics Based Region Growing Segmentation Method for Ultrasound Medical Images, International Journal of Medical, Health, Pharmaceutical and Biomedical Engineering Vol.1, n.10, pp.570-575, 2007.
Elnomery Zanaty and Sultan Aljahdali, Improving Fuzzy Algorithms for Automatic Magnetic Resonance Image Segmentation, The International Arab Journal of Information Technology, Vol. 7, n.3, pp.271-279, 2010.
Mohamed N. Ahmed, Sameh M. Yamany, Nevin Mohamed, Aly A. Farag, and Thomas Moriarty, A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data, IEEE Transactions on Medical Imaging, Vol. 21, n. 3, pp.193-199, 2002.
Jianzhong Wangm, Jun Kong, Yinghua Lu,,Miao Qi, Baoxue Zhanga, A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints, Computerized Medical Imaging and Graphics, Vol. 32, n.8, pp.685–698, 2008.
Paresh Chandra Barman, Sipon Miah, Bikash Chandra Singh and Mst. Titasa Khatun, MRI mage segmentation using level set method and implement an medical Diagnosis system, Computer Science & Engineering: An International Journal (CSEIJ), Vol.1, n.5, pp.1-10, 2011.
Shan Shen, William Sandham, Member, IEEE, Malcolm Granat, and Annette Sterr , MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization, IEEE Transactions On Information Technology In Biomedicine, Vol. 9, n.3, pp.459-467, 2005.
Iraky khalifa , Aliaa Youssif , Howida Youssry, MRI Brain Image Segmentation based on Wavelet and FCM Algorithm, International Journal of Computer Applications, Vol.47, n.16, pp.32-39, 2012.
Chung-Yi Huang, Lai-Jun Luo, Pei-Yuan Lee, Jiing-Yih Lai,,Wen-Teng Wang, Shang-Chih Lin, Efficient Segmentation Algorithm for 3D Bone Models Construction on Medical Images, Journal of Medical and Biological Engineering, Vol.31, n.6, pp.375-386, 2010.
A .Morenoa, C.M. Takemuraa, O .Colliotc ,O .Camarad, I .Blocha, Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images, Pattern Recognition,Vol.41,n.8, pp. 2525 – 2540, 2008.
X.M. Pardo. , M.J. Carreira , A. Mosquera, D. Cabello, A snake for CT image segmentation integrating region and edge information, Image and Vision Computing,Vol.19, n.7, pp.461-475, 2001.
Zikuan Chen, Sabee Molloi, Automatic 3D vascular tree construction in CT angiography, Computerized Medical Imaging and Graphics,Vol.27, pp.469–479,2003.
Yaser Ajmal Sheikh, Erum Arif Khan, Takeo Kanade, Mode-seeking by Medoidshifts. Computer Vision (ICCV), IEEE International conference on, pp.1-8,2007.
V.V.Gomathi, Dr.S.Karthikeyan, An Efficient Clustering based Segmentation Algorithm for Computer Tomography Image Segmentation, Journal of biomedical engineering and medical imaging, vol.1, n.3, pp. 1-11, 2014.
J.L Marroquin, F. Girosi, Some Extentions of the K-Means Algorithm For Image Segmentation and Pattern Classification, Technical Report, MIT Artificial Intelligence Laborartory,1993.
M.Luo, Y.F.Ma ,H.J. Zhang, A Special Constrained K-Means approach to Image Segmentation ,proceedings of the Fourth International Conference on Information Communications and Signal Processing and the Fourth Pacific Rim Conference on Multimedia,Vol.2,pp.738-742,2003. V.V. Gomathi, S. Karthikeyan, Performance Analysis of Distance Measures for Computer tomography Image Segmentation, International Journal of Computer Technology and Applications, Vol. 5, n.2, pp. 400-405,2014.
Beecks.C, Uysal M.S, Seidl.T, Signature Quadratic Form Distances for Content-based Similarity, ACM CVIR 2010.
V.V. Gomathi , S. Karthikeyan, A Proposed Hybrid Medoid Shift with K-Means (HMSK) Segmentation Algorithm to Detect Tumor and Organs for Effective Radiotherapy, Lecture Notes in Computer Science(Springer), Vol. 8284, pp.139-147, 2013.
Ebrahim, M.J., Pourghassem, H., A novel automatic synthetic segmentation algorithm based on mean shift clustering and canny edge detector for aerial and satellite images, (2012) International Review on Computers and Software (IRECOS), 7 (3), pp. 1122-1129.
Ali Hassan Al-Fayadh, Hind Rostom Mohamed ,Raghad Saaheb Al-Shimsah, CT Angiography Image Segmentation by Mean Shift Algorithm and Contour with Connected Components Image, International Journal of Scientific & Engineering Research, Vol.3, n. 8, pp.1-5, 2012.
Keh-Shih Chuang , Hong-Long Tzeng , Sharon Chen , Jay Wu , Tzong-Jer Chen, Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics,Vol.30,n.1, pp. 9–15, 2006.
Wenbing Tao, Hai Jin, Yimin Zhang, ―Color Image Segmentation Based on Mean Shift and Normalized Cuts, IEEE Transactions on systems, man, and cybernetics—part b: Cybernetics, Vol. 37, n. 5, pp.1382-1389, 2007.
Zhou Wang and Alan C. Bovik, Ligang Lu, Why is image Quality Assessment So Difficult.
Zhou Wang, Member, Alan C. Bovik, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions On Image Processing, Vol. 13, n. 4, pp.1-14, 2004.
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