Automatic Diagnosis of Liver Tumor in Ct Images
T. Dhiliphan Raj Kumar1, D. Deepa2, J. Jeyaranjani3
1Dr. T. Dhiliphan Raj Kumar, Assistant Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2Mrs. D. Deepa, Assistant Professor, Department of Computer Science, Kongu Engineering College, (Tamil Nadu), India.
3Mrs. J. Jeyaranjani, Assistant Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 25 November 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 30 December 2019 | PP: 1105-1109 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A11161291S419/19©BEIESP | DOI: 10.35940/ijeat.A1116.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Medical Image Segmentation is the important tool for diagnosing tumor and for planning how to do treatment. The intention of this study is to detect tumor from CT liver images. Initially, liver is segmented from abdomen CT images. Then SVM Classification is included to classify the normal and abnormal liver structure. If it is abnormal then the tumor will be segmented from liver structure. This technique is computed using sensitivity, specificity and accuracy and is providing good result.
Keywords: Tumor Detection, Liver Segmentation, SVM Classification, Hepatic Tumor.
Scope of the Article: Signal and Image Processing