An Efficient Clustering based Segmentation Algorithm for Computer Tomography image Segmentation
Colossal amount of research has been done in creating many different approaches and algorithms for medical image segmentation, but it is still complicated to evaluate all the images. However the problem remains challenging, with no general and unique solution in computer-aided diagnosis. This paper provides medical image segmentation based on Clustering for computer tomography images. In this paper, we consider a mean shift segmentation and medoid shift segmentation method. We validate the mean shift and medoid shift medical image segmentation approach with the parameters in terms of sensitivity, specificity and accuracy. The Real time dataset is used to evaluate the performance of the proposed method. The experimental result shows that the medoid shift segmentation method gives more accurate and robust segmentation results than mean shift segmentation method.
Linda G. Shapiro and George C. Stockman., Computer Vision. 2001, New Jersey: Prentice-Hall. p.279-325.
Neeraj Sharma and Lalit M. Aggarwal, Automated medical image segmentation technique. Journal of Medical Physics. 2010. 35(1):p.3–14.
Lior Shapira, Shai Avidan , Ariel Shamir, Mode-Detection via Median-Shift, Computer Vision. IEEE 12th International conference on, 2009. p.1909-1916.
Konstantinos G. Derpanis, Mean Shift Clustering, 2005.
Miaoqing Huang, Liang Men, Chenggang Lai, Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms. Ed: Xuan Shi, Volodymyr Kindratenko, Chaowei Yang. Modern Accelerator Technologies for Geographic Information Science, 2013. p.157-166.
Dorin Comaniciu, Peter Meer, Mean Shift: A Robust Approach toward Feature Space Analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002. 24(5): p.603-619.
Tobias Weyand and Bastian Leibe, Discovering Details and Scene Structure with Hierarchical Iconoid Shift. Computer vision (ICCV), IEEE International conference on, 2013. p. 3479-3486.
Yaser Ajmal Sheikh, Erum Arif Khan, Takeo Kanade, Mode-seeking by Medoidshifts. Computer Vision (ICCV), IEEE International conference on, 2007. P.1-8.
Kaufman.L and P. J. Rousseeuw. Clustering by means of medoids. Statistical Data Analysis Based on the L1 Norm. Y.Dodge, Ed., Northi Holand /Elsevier.1987. p.405-416.
MacQueen.J. Some methods for classification and analysis of multivariate observations. Mathematical Statistics and Probability, Proceedings of 5th Berkeley Symposium on, 1967, 1(1): p.281-297.
Mohammad Talebi, Ahamd Ayatollahi, Ali Kermani, Medical ultrasound image segmentation using genetic active contour. Journal of Biomedical Science and Engineering, 2011. 4: p.105-109.
Yizong Cheng, Mean Shift, Mode Seeking, and Clustering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1995.17(8): p.790-799.
Arnaldo Mayer and Hayit Greenspan, An Adaptive Mean-Shift Framework for MRI Brain Segmentation. Medical Imaging IEEE Transactions on, 2009. 28(8): p.1238-1250.
Jinghua Lu, Jie Chen, Juan Zhang, Lihui Zou, Medical Image Segmentation Using Mean Shift Algorithm and General Edge Detection. 18th IFAC World Congress Milano (Italy), 2011. p.9656-9661.
Comaniciu. D and P. Meer, Mean shift analysis and applications. Computer Vision, International Conference on, 1999. pp. 1197–1203.
Comaniciu. D, V. Ramesh, and P. Meer, Real-time tracking of non-rigid objects using mean shift.Computer Vision and Pattern Recognition, IEEE Conference on, 2000. 2: p.142–149.
Beleznai.C, B. Frühstück, H. Bischof, Human Tracking by Fast Mean Shift Mode Seeking, Journal of Multimedia, 2006.1(1): p.1-8.