Segmentation of Liver from Abdomen CT images and 3D Visualization


  • Deepti Mittal Department of Electrical and instrumentation Engineering Thapar University, Patiala, Punjab, India



Active contour, marching cube


Segmentation of liver is the initial and fundamental step for the diagnosis of liver disease, 3-D volume construction and volume measurement. However, segmentation of liver is a challenging task due to it’s inter and intra intensity and texture similarities among other organs present in CT abdominal images. A semiautomatic method has been proposed to segment the liver portion from CT abdominal images and their three dimensional volume construction by (i)Noise removal using median filter,(ii)Segmentation of liver portion based on active contour method using sparse field method and(iii)liver volume construction using marching cube method. Evaluation of proposed method is carried out on clinically acquired CT images and effectiveness of algorithm is evaluated by comparing manually segmented liver portion marked by radiologist with proposed method

Author Biography


electrical and instrumentation engineering department

ME student


(1) Corporate Center: American Cancer Society Inc. 250 Williams Street, NW, Atlanta, GA 30303-1002 (404) 320-3333.

(2) C. Ananth, D.L.R. Bai , K. Renuka, C. Savithra, A. Vidhya, Interactive Automatic Hepatic Tumor CT Image Segmentation, International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-3, Issue-1),2014.

(3) S.S. Kumar, R.S. Moni, J. Rajeesh, Automatic Segmentation of Liver and Tumor for CAD of Liver, Journal of advances in information technology, vol. 2, no. 1, February 2011.

(4) M. Jayanthi and B. Kanmani, Extracting the Liver and Tumor from Abdominal CT Images, 2014 Fifth International Conference on Signals and Image Processing 978-0-7695-5100-5/13 $31.00 © 2013 IEEE.

(5) D. Mittal and K. Kumari, Automated detection and segmentation of drusen in retina fundus images, Computers and Electrical Engineering 47 (2015) 82–95.

(6) S.-J. Lim, Y.-Y. Jeong, Y.-S. Ho, Automatic liver segmentation for volume measurement in CT images, 1047-3203/$-see front matter 2005 Elsevier Inc.

(7) R. Rajagopal and P. Subbaiah, A survey on liver tumor detection and segmentation methods, ARPN Journal of Engineering and Applied Sciences Vol. 10, NO. 6, April 2015.

(8) N.H. Abdel-massieh, M.M. Hadhoud, and K.A. Moustafa, A fully automatic and efficient technique for liver segmentation from abdominal CT images, presented at Informatics and Systems (INFOS), 2010 The 7th International Conference on, 2010.

(9) S. Casciaro, R. Franchini, L. Massoptier, E. Casciaro, F. Conversano,A. Malvasi and A. Lay-Ekuakille, Fully Automatic Segmentations of Liver and Hepatic Tumors From 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two

Automatic Methods, IEEE Sensors journal, vol. 12, no. 3, March 2012.

(10) L. Massoptier, S. Casciaro, A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans, Euro Radial (2008).

(11) O.F. Abd-Elaziz, M.S. Sayed and M.I. Abdullah, Liver Tumors Segmentation from Abdominal CT Images using Region Growing and Morphological Processing, 978-1-4799-5807-8/14/$31.00 @2014 IEEE.

(12) K. Sharma and D. Mittal, Contrast Enhancement Technique for CT Images, Journal of Biomedical Engineering and Medical Imaging, Volume 2, No 1, Feb (2015) , pp 44-50.

(13) A. Krishan, D. Mittal, International Journal on Recent Technologies in Mechanical and Electrical Engineering (IJRMEE), Volume: 2 Issue: 5, May 2015.

(14) P. Campadelli, E. Casiraghi, S. Pratissoli and G. Lombardi, Automatic Abdominal Organ Segmentation from CT images, Electronic Letters on Computer Vision and Image Analysis 8(1):1-14, 2009.

(15) A.A. Moghe, J. Singhai, S.C Shrivastava, Automatic Threshold based Liver Lesion Segmentation in Abdominal 2D-CT Images, International Journal of Image Processing (IJIP), Volume (5): Issue (2):

(16) V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours, International Journal of Computer Vision, Volume 22, Issue 1, pp. 61-79, 1997.

(17) M. Kass, Witkin and Terzopolous, Snakes: Active contour models, International Journal of Computer Vision, pages 321-331, 1987.

(18) R. T. Whitaker, A level-set approach to 3D reconstruction from range data, International Journal of Computer Vision, Volume 29, Issue 3, pp.203-231, 1998.

(19) T.F. Chan and L.A. Vese, Active contours without edge, IEEE Transactions on Image Processing, Volume 10, Issue 2, pp. 266-277, 2001.

(20) T. Heimann and H.P. Meinzer, Statistical shape models for 3D medical image segmentation: A review, Medical Image Analysis 13 (2009) 543–563, 1361-8415/$.

(21) W. E. Lorensen and H.E. Cline, Marching cubes: A high resolution 3D surface construction algorithm, Computer Graphics, Volume 21, Number 4, July 1987.




How to Cite

Thakur, R., & Mittal, D. (2015). Segmentation of Liver from Abdomen CT images and 3D Visualization. British Journal of Healthcare and Medical Research, 2(5), 46.

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