Automatic Liver Cancer Segmentation using active contour model and RF Classifier
AT.K.R. Agita1, M. Moorthi2
1T.K.R.Agita, Department of Electronic and Communication Engineering, Anna University, Chennai, India.
2Dr.M.Moorthi, Department of Medical Electronics, Saveetha Engineering College, Chennai, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 1139-1142 | Volume-8 Issue-11, September 2019. | Retrieval Number: J90310881019/2019©BEIESP | DOI: 10.35940/ijitee.J9031.0981119
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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: SIn this paper, we presented the new method for liver cancer detection. Computed Tomography (CT) has becomes important tool for diagnosis of liver cancer. The proposed method used in this paper is Random Forest (RF) classifier algorithm for the detection of cancer in the liver. For the automatic segmentation, here we use active contour method to segment the liver and liver cancer to rectify the manual segmentation problem. It is fully automatic and the proposed classifier will successfully classifies whether it is malignant or benign liver cancer tumor. Manual identification is not accurate and also time consuming task. The new method proposed in this paper will segment the liver cancer from the CT image of liver automatically. It is highly accurate and less computation time. The experiment results show the accuracy of the proposed method. Random Forest classifier has 91% accuracy rate and less error rate and achieved excellent test result.
Keywords: liver cancer, active contour segmentation, Computed Tomography (CT), Random Forest (RF).
Scope of the Article: