Abstract
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.
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Al-Fayadh, A.H., Mohamed, H.R., Al-Shimsah, R.S.: CT Angiography Image Segmentation by Mean Shift Algorithm and Contour with Connected Components Image. International Journal of Scientific & Engineering Research 3(8) (2012) ISSN 2229-5518
Nadernejad, E., Sharifzadeh, S.: A New method for image segmentation based on Fuzzy C-Means algorithm on pixonal images formed by bilateral filtering. Springer (2011)
Moftah, E., El-Bendary, Hassanien: Performance evaluation of computed tomography liver image segmentation approaches. In: International Conference on Hybrid Intelligent Systems, HIS (December 2012)
Masoumia, H., Behradb, A., Pourminaa, M.A., Roostac, A.: Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomedical Signal Processing and Control (2012)
Liu, J., Wang, Z., Zhang, R.: Liver Cancer CT Image Segmentation Methods Based on Watershed Algorithm. In: International Conference on Computational Intelligence and Software Engineering (December 2009)
Zhang, K., Zhang, L., Song, H., Zhou, W.: Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing, 668–676 (2010)
Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, 9–15 (2006)
Shapiro, L.G., Stockman, G.C.: Computer Vision, pp. 279–325. Prentice-Hall, New Jersey (2001) ISBN 0-13-030796-3
Wang, L., Li, C., Sun, Q., Xia, D., Kao, C.-Y.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Computerized Medical Imaging and Graphics, 520–531 (2009)
Sayadi, M., Tlig, L., Fnaiech, F.: A New Texture Segmentation Method Based on the Fuzzy C-Mean Algorithm and Statistical Features. Applied Mathematical Sciences 1 (2007)
Shojaii, R., Alirezaie, J., Babyn, P.: Automatic lung segmentation in CT images using watershed transform. In: International Conference on Image Processing (2005)
Tsai, A., Wells, W., Tempany, C., Grimson, E., Willsky, A.: Mutual information in coupled multi-shape model for medical image segmentation. Medical Image Analysis, 429–445 (2004)
Tao, W., Jin, H., Zhang, Y.: Color Image Segmentation Based on Mean Shift and Normalized Cuts. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 37(5) (2007)
Tao, W., Tai, X.-C.: Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization. Image and Vision Computing, 499–508 (2011)
Xiang, Y., Chung, A.C.S., Ye, J.: An active contour model for image segmentation based on elastic interaction. Journal of Computational Physics, 455–476 (2006)
Huang, Y.-L., Chen, D.-R.: Watershed Segmentation For Breast Tumor In 2-D Sonography. Ultrasound in Med. & Biol. 30(5), 625–632 (2000)
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Gomathi, V.V., Karthikeyan, S. (2013). A Proposed Hybrid Medoid Shift with K-Means (HMSK) Segmentation Algorithm to Detect Tumor and Organs for Effective Radiotherapy. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_15
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DOI: https://doi.org/10.1007/978-3-319-03844-5_15
Publisher Name: Springer, Cham
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