Investigating Hepato-Cellular-Carcinoma Based on CT-scan Tumor Edge Detection

International Journal of Medical Science
© 2017 by SSRG - IJMS Journal
Volume 4 Issue 3
Year of Publication : 2017
Authors : Chethan K S
How to Cite?

Chethan K S, "Investigating Hepato-Cellular-Carcinoma Based on CT-scan Tumor Edge Detection," SSRG International Journal of Medical Science, vol. 4,  no. 3, pp. 31-35, 2017. Crossref,


Abnormalities in the liver include masses which can be benign or malignant. Due to the presence of these abnormalities, the regularity of the liver structure is altered, which changes its fractal dimension. In this paper, Hepato cellular carcinoma liver tumor is detected automatically using Computed Tomography Images. The method proposed has three stages. In the first stage, all kind of noises are such as speckles removed using image filtering. The overlap between different peaks is a strong evidence of noisy image. In the second stage, hepato cellular carcinoma tumor candidates are detected using histogram based analysis and K-mean based analysis. Suspected area was recognized successfully as the outcome of histogram based analysis. Tumor pattern shows gradual change from dark to light. The darker tune means worse damage as well as older damage compared to the lighter tune. The dark tune indicates severity and age. The light tune indicates new development of the tumor. Quantitative evaluation was done using ANOVA single factor test analysis to test whether there is any significant relation between the classes. Since, P < 0.05, there is insignificant relation between all the classes and we reject the null hypothesis. Further, validation between manual and automated segmentation was made and it is found that the error between manual segmentation and automated segmentation is smaller than 8.2 % which shows an evidence of success. In the final stage, the performance capability of K-means versus HBAA was analyzed. The error percentage in (HBAA) is (8.2 %), while in (K-mean classifier) the 139.4 %. The estimated area by (K-mean classifier) was exaggerated to more than double. The estimated area by (HBAA) was 92 % of the calculated area by the radiologist. The result is a proof of the superiority of (HBAA) over (K-mean classifier).


Medical Imaging, Liver Tumor Segmentation, K-Mean, HBAA.


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