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, https://doi.org/10.14445/23939117/IJMS-V4I3P107
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.
 World Cancer Report, World Health Organization, pp. Chapter 1.1, ISBN 9283204298, (2014).
 S. A. Khan, B. R. Davidson, R. D. Goldin, N. Heaton, J. Karani, S. P. Pereira, W. M. Rosenberg, P. Tait, S. D. Taylor- Robinson, A. V. Thillainayagam, H. C. Thomas, H. Wasan, Guidelines for the diagnosis and treatment of cholangio carcinoma: an update, British Society of Gastroenterology (December2012),Gut61(12):pp.1657-69. doi:10.1136/gutjnl- 2011-301748, PMID 22895392.
 Maton, Anthea, Jean Hopkins, Charles William McLaughlin, Susan Johnson, Maryanna Quon Warner, David LaHart, D. Jill Wright, Human Biology and Health, Englewood Cliffs, New Jersey, USA: Prentice Hall. ISBN 0-13-981176-1, OCLC 32308337, (1993).
 Mortality and Causes of Death (2013), Collaborators (Dec. 17, 2014), Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013, Lancet 385: pp. 117–71. doi:10.1016/S0140-6736(14)61682-2, PMC 4340604, PMID 25530442.
 Margini C, Dufour J F, The story of HCC in NAFLD: From epidemiology, across pathogenesis, to prevention and treatment liver, Int 2016: 36:317-324.
 Sakai H, Shirakami Y, Shimizu M, Chemeprevention of Obesity-related Liver Carcinogenesis by using Pharmaceutical and nutraceutical agents, World J Gastroenterol 2016; 22:394-406.
 Shimizu M, Tanaka T, Moriwaki H, Obesity and hepatocellular Carcinoma: targeting obesity-related inflammation for chemoprevention of liver carcinogenesis, Semin Immunopathol 2013; 35:191-202.
 Chen H, Shieh J, Chang C, Chen T, Lin J, Wu M, Wu C, Metformin decreases hapatocellular carcinoma risk in a dose-dependent manner: Population- based and in vitro studies, Gut 2013; 62:606-615.
 Li Y, Liu L, Wang B, Wang J, Chen D, Metaformin in nonalcoholic fatty liver disease: A Systematic review & metaanalysis, Biomed Rep 2013;1:57-64.
 Singh, Singh PP, Singh AG, Murad M H, Sanchez W, Antidiabetic medications and the risk of hepatocellular cancer: A Systematic review and meta-analysis, Am J Gastroenterol 2013; 108:881-891;quiz 892.
 Morrison MC, Mulder P, Salic K, Vreheij, Liang W, Van Duyvenvoorde W, Menke A, Kooistra J, Kleemann R, Wielinga P Y, Intervention with a caspase-1 inhibitor reduces obesity-associated hyperinsulinemia, non alcoholic steatohepatitis (NASH) & hepatic fibrosis in LDLR, Int Jobes(London) 2016, Epub ahead of print.
 Barreyro F J, Holod S, Finocchietto P V, Camino A M, Aquino J B, Carreras M C, Poderoso J J, Gores G J, The pancaspase inhibiotr Emricasan (IDN-6556) decreases liver injury and fibrosis in a murine model of non-alcoholic staetohepatitis, Liver Int 2015;35:953-966.