An Efficient Clustering based Segmentation Algorithm for Computer Tomography image Segmentation

  • V.V. Gomathi Chandramohan Ph.D Research Scholar Research and Development Center Bharathiar University Coimbatore Tamilnadu India
  • D Subramaniam Karthikeyan Assistant Professor, Department of Information Technology, College of Applied Sciences, Sohar, Oman
Keywords: Clustering, Computer tomography, Segmentation, Mean shift segmentation, Medoid shift segmentation

Abstract

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.

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Published
2014-06-30
How to Cite
Gomathi Chandramohan, V., & Subramaniam Karthikeyan, D. (2014). An Efficient Clustering based Segmentation Algorithm for Computer Tomography image Segmentation. Journal of Biomedical Engineering and Medical Imaging, 1(3), 01-11. https://doi.org/10.14738/jbemi.13.267