Efficient Image Processing Based Liver Cancer Detection Method

Namrata Ghuse¹ , Yogita Deore² , Amol Potgantwar³

¹ Computer Engineering Department, SITRC (Pune University), Nashik, India.
² Computer Engineering Department, SITRC (Pune University), Nashik, India.
³ Computer Engineering Department, SITRC (Pune University), Nashik, India.

Correspondence should be addressed to: yogitadeore111@gmail.com.

Section:Research Paper, Product Type: Journal
Vol.5 , Issue.3 , pp.33-38, Jun-2017

Online published on Jun 30, 2017

Copyright © Namrata Ghuse, Yogita Deore, Amol Potgantwar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

1399 Views    584 Downloads    472 Downloads

Abstract :
Cancer treatment has a great significance due to the prevalent episodes of the diseases, very high death rate and reappearance after treatment. In the large world scale, cancer stands in the fifth position which causes death. Among the various cancers, liver cancer stands in the third position. Liver cancer is generally diagnosed by three different test like blood test, image test and biopsy. To make the different task of detecting the liver cancer simpler, less time consuming, an effective and efficient approach is adopted for the same. In this research an Image processing system for detecting liver cancer is put forward. The proposed detection methodology makes use of MRI and CT. Region growing technique is adopted so as to segment the images in order to capture the region of interest. Later, wavelet transform is considered to compute the threshold values for the region of interest. After processing and measuring it gives the correct result with in the efficient time period.

Key-Words / Index Term :
Image; Region Growing; CT Image; Image processing; Segmentation

References :
[1] Nelofar Kureshi, Syed Sibte Raza Abidi, “A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer�, IEEE Journal of Health Informatics Vol. 20, No.1, pp. 424-431, 2016.
[2] PR Anisha, CKK Reddy, LVN Prasad, "A pragmatic approach for detecting liver cancer using image processing and data mining techniques", International Conference on Signal Processing And Communication Engineering Systems (SPACES), India, pp.1-6, 2015.
[3] F. J. Kaye, N. Lindeman, T. J. Boggon, K. Naoki, H. Sasaki, Y. Fujii, M. J., W. R. Sellers, B. E. Johnson, M. Meyerson, “EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy�, Science, vol. 304, no. 5676, pp. 1497-1500, Jun. 2004.
[4] F. G. Haluska, D. N. Louis, D. C. Christiani, J. Settleman, D. A. Haber, “Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib�, New England J. Med., vol. 350, no. 21, pp. 2129-2139, 2004.
[5] H. B. Kekre, A. Bild, E. S. Iversen, A. T. Huang, J. R. Nevins, M.West, “Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes�, Proc. Nat. Acad. Sci. vol. 101, no. 22, pp. 8431-8436, 2004.
[6] Nelofar Kureshi, L. X. Li, “Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information�, Bioinformatics, vol. 22, no. 4, pp. 466-471, 2006.
[7] A. J. Stephenson, A. Smith, M. W. Kattan, J. Satagopan, V. E. Reuter, P. T. Scardino, W.L. Gerald, “Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy�, Cancer, vol. 104, no. 2, pp. 290-298, 2005.
[8] C. C. Bennett, T. W. Doub, R. Selove, “EHRs connect research and practice: Where predictive modeling, artificial intelligence, and clinical decision support intersect�, Health Policy Technol., vol. 1, no. 2, pp. 105-114, 2012.
[9] L. Chouchane, R. Mamtani, A. Dallol, J. I. Sheikh, “Personalized medicine: a patient-centered paradigm�, J. Trans. Med., vol. 9, Issue.1, pp.201-206, 2011.
[10] S. Navada, P. Lai, A. G. Schwartz, G.P. Kalemkerian, “Temporal trends in small cell lung cancer: Analysis of the national surveillance epidemiology and end-results (SEER) database�, J. Clin. Oncol., vol. 24, no. 18, p. 70-82, 2006.
[11] R. S. Herbst, M. Fukuoka, J. Baselga, “Gefitinib-A novel targeted approach to treating cancer�, Nature Rev. Cancer, vol. 4, no. 12, pp. 956-965, 2004.
[12] Movsas, A.L. Stiegler, T.J. Boggon, S. Kobayashi, B. Halmos, “EGFR-mutated lung cancer: A paradigm of molecular oncology�, Oncotarget, vol. 1, no. 7, pp. 497-514, 2010.
[13] Yasser M. Kadah, Einhorn LH, Bond WH, Hornback N, Joe BT, “Long-term results in combinedmodality treatment of small cell carcinoma of the lung�, Semin Oncol, Vol. 5, No. 3, pp. 309-313, 1978
[14] Govindan R, Page N, Morgensztern D, Read W, Tierney R, Vlahiotis A, “Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database�, J Clin Oncol, Vol.24, Issue.28, pp.4539-4544, 2006.
[15] Murray N, Turrisi AT, “A review of first-line treatment for small-cell lung cancer�, J Thorac Oncol, Vol.1, Issue.3, pp.270278, 2006.
[16] Ravdin PM, Cronin KA, Howlader N, Berg CD, Chlebowski RT, Feuer EJ, Edwards BK, Berry DA., “The decrease in breast-cancer incidence in 2003 in the United States�, New England Journal of Medicine, Vol.356, Issue.16, pp.1670-1674, 2007.
[17] Green RA, Humphrey E, Close H, Patno ME, “Alkylating agents in bronchogenic carcinoma�, Am J Med, Vol.46, Issue.4, pp.516-525, 1969.
[18] Chung-Ming Wu, Weiss RB, “Small-cell carcinoma of the lung: therapeutic management�, Ann Intern Med, Vol.88, Issue.4, pp.522–31, 1978.
[19] Nelofar Kureshi, Syed Sibte Raza Abidi, “A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer�, IEEE Journal of Health Informatics Vol. 20, No.1, pp. 424-431, 2016.
[20] J.G. Paez, P.A. Janne, J.C. Lee, S. Tracy, H. Greulich, S. Gabriel, P. Herman, F.J. Kaye, N. Lindeman, T.J. Boggon, K. Naoki, H. Sasaki, Y. Fujii, M.J., W.R. Sellers, B.E. Johnson, M. Meyerson, “EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy�, Science, vol. 304, no. 5676, pp. 1497-1500, 2004.
[21] D.C. Christiani, J. Settleman, D.A. Haber, “Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib�, New England J. Med., vol.350, no.21, pp.2129-2139, 2004.
[22] C.M. Chen, A. Bild, E.S. Iversen, A.T. Huang, J.R. Nevins, M. West, “Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes�, Proc. Nat. Acad. Sci., vol.101, no.22, pp. 8431-8436, 2004.
[23] L. X. Li, “Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information�, Bioinformatics, vol. 22, no. 4, pp. 466-471, 2006.
[24] A.J. Stephenson, A. Smith, M.W. Kattan, J. Satagopan, V.E. Reuter, P.T. Scardino, W.L. Gerald, “Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy�, Cancer, vol. 104, no. 2, pp. 290-298, 2005.
[25] C.C. Bennett, T.W. Doub, R. Selove, “EHRs connect research and practice: Where predictive modeling, artificial intelligence, and clinical decision support intersect�, Health Policy Technol., vol. 1, no. 2, pp. 105-114, 2012.
[26] L. Chouchane, R. Mamtani, A. Dallol, J.I. Sheikh, “Personalized medicine: a patient-centered paradigm�, J. Trans. Med., vol. 9, Issue.1, pp.201-206, 2011.
[27] S. Navada, P. Lai, A.G. Schwartz, G.P. Kalemkerian, “Temporal trends in small cell lung cancer: Analysis of the national surveillance epidemiology and end-results (SEER) database�, J. Clin. Oncol, vol.24, no.18, pp.70-82, 2006.
[28] R. S. Herbst, M. Fukuoka, J. Baselga, “Gefitinib—A novel targeted approach to treating cancer�, Nature Rev. Cancer, vol. 4, no. 12, pp. 956-965, 2004.
[29] Z. Zhang, A. L. Stiegler, T. J. Boggon, S. Kobayashi, B. Halmos, “EGFR-mutated lung cancer: A paradigm of molecular oncology�, Oncotarget, vol. 1, no. 7, pp. 497-514, 2010.
[30] Einhorn LH, Bond WH, Hornback N, Joe BT, “Long-term results in combinedmodality treatment of small cell carcinoma of the lung�, Semin Oncol, Vol.5, Issue.3, pp.309-313, 1978.
[31] Govindan R, Page N, Morgensztern D, Read W, Tierney R, Vlahiotis A, “Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database�, J Clin Oncol, Vol.24, Issue.28, pp.4539-44, 2006
[32] Murray N, Turrisi AT, “A review of first-line treatment for small-cell lung cancer�, J Thorac Oncol, Vol.1, Issue.3, pp.270-278, 2006
[33] Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ., “Cancer statistics�, CA Cancer J Clin, Vol.57, Issue.4, pp.43-66, 2007
[34] Green RA, Humphrey E, Close H, Patno ME, “Alkylating agents in bronchogenic carcinoma�, Am J Med, Vol.12, Issue.6, pp.516-525, 2007.