Abstract— Computer aided diagnosis of liver tumors from abdominal Computer Tomography (CT) images requires segmentation and analysis of tumor. Automatic segmentation of tumor from CT images is difficult, due to the size, shape, position and presence of other objects with the same intensity present in the image. Therefore, it is necessary to segment the liver first so that tumor can then be segmented accurately from it. Liver and tumor segmentation can be performed on the CT image manually or semi automatically. In this paper, an approach for automatic segmentation of liver and tumor from CT images mainly used for computer-aided diagnosis of liver is proposed. The method uses regiongrowing, facilitated by pre and post processing functions for automatic segmentation of liver and Alternative Fuzzy C-Means (AFCM) algorithm for tumor segmentation. The effectiveness of the algorithm is evaluated by comparing automatic segmentation results to the manual segmentation results. Quantitative comparison shows a close correlation between the automatic and manual as well as high spatial overlap between the regions-ofinterest (ROIs) generated by the two methods.
Index Terms—Computer Aided Diagnosis, Liver segmentation, Tumor Segmentation, Region Growing, Alternative FCM
Cite: S.S. Kumar, R.S. Moni, and J. Rajeesh, "Automatic Segmentation of Liver and Tumor for CAD of Liver," Journal of Advances in Information Technology, Vol. 2, No. 1, pp. 63-70, February, 2011.doi:10.4304/jait.2.1.63-70
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