A Proposed Hybrid Medoid Shift with K-Means (HMSK) Segmentation Algorithm to Detect Tumor and Organs for Effective Radiotherapy

  • V. V. Gomathi
  • S. Karthikeyan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


Image segmentation plays a significant role in many medical imaging applications. Manual segmentation of medical image by the radiologist is not only a tiresome and time consuming process, also not a very accurate with the increasing medical imaging modalities and unmanageable quantity of medical images that need to be examined. Therefore it is essential to examine current methodologies of image segmentation. Enormous research has been done in creating many different approaches and algorithms for medical image segmentation, but it is still difficult to evaluate all the images. However the problem remains challenging, with no general and unique solution. This paper reviews some existing medical image segmentation algorithms suitable for CT images. Their pros and cons were analyzed and proposed a HMSK algorithm for slices of CT images to give effective radiation therapy.


Computed Tomography Segmentation Active Contour Model Watershed Segmentation K-Means Fuzzy CMeans Mean Shift Segmentation Medoid Shift Segmentation 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • V. V. Gomathi
    • 1
  • S. Karthikeyan
    • 2
  1. 1.Research and Development CentreBharathiar UniversityCoimbatoreIndia
  2. 2.Department of Information TechnologyCollege of Applied SciencesSoharOman

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