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Reseach Article

Automatic Liver Segmentation from Abdominal MRI Images using Active Contours

by Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 1
Year of Publication: 2017
Authors: Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa
10.5120/ijca2017915512

Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa . Automatic Liver Segmentation from Abdominal MRI Images using Active Contours. International Journal of Computer Applications. 176, 1 ( Oct 2017), 30-37. DOI=10.5120/ijca2017915512

@article{ 10.5120/ijca2017915512,
author = { Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa },
title = { Automatic Liver Segmentation from Abdominal MRI Images using Active Contours },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 176 },
number = { 1 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number1/28519-2017915512/ },
doi = { 10.5120/ijca2017915512 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:23.959818+05:30
%A Roaa G. Mohamed
%A Noha A. Seada
%A Salma Hamdy
%A Mostafa G. M. Mostafa
%T Automatic Liver Segmentation from Abdominal MRI Images using Active Contours
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 1
%P 30-37
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a system for the automatic segmentation of the liver from Magnetic Resonance Images (MRI). The system works without the need for setting manual seed points or setting a region of interest. Instead, the proposed system automatically detects and segments the liver through relying on its anatomical features for detection and using active contour for segmentation. The proposed segmentation system begins with localizing the liver or a part of it from a given MRI image using biggest components analysis. The extracted liver part is later used as a mask for full liver segmentation using active contour. The proposed system is fully automatic, works on different cases of MRI images (different sizes, healthy and abnormal liver). The detection and segmentation of the liver succeeded in 95% of the test cases acquired from different MRI imaging modalities.

References
  1. D. Market, S. Of, and A. Ferula, “Available online through,” vol. 1, no. 2, pp. 12712–12717, 2010.
  2. T. M. Hassan and M. Elmogy, “Medical Image Segmentation for Liver Diseases : A Survey,” vol. 118, no. 19, pp. 38–44, 2015.
  3. S. V Chavan, “Study and Analysis of Image Segmentation Techniques for Food Images,” vol. 136, no. 4, pp. 20–24, 2016.
  4. G. S. Chandel, R. Kumar, D. Khare, and S. Verma, “Analysis of Image Segmentation Algorithms Using MATLAB,” Int. J. Eng. Innov. Res., vol. 1, no. 1, pp. 2277–5668, 2012.
  5. M. Waseem Khan, “A Survey: Image Segmentation Techniques,” Int. J. Futur. Comput. Commun., vol. 3, no. 2, pp. 89–93, 2014.
  6. G. G. Rajput, “Automatic Detection of Abnormalities Associated with Abdomen and Liver Images : A Survey on Segmentation Methods,” vol. 140, no. 4, pp. 1–9, 2016.
  7. S. Mandiratta, “A Perlustration of Various Image Segmentation Techniques,” vol. 139, no. 12, pp. 26–31, 2016.
  8. A. Taneja, P. Ranjan, and A. Ujjlayan, “A Performance Study of Image Segmentation Techniques,” IEEE Trans. Image Process., 2015.
  9. N. R. Pal and S. K. Pal, “A Review on Image Segmentation Techniques,” Pr, vol. 26, no. 9, pp. 1277–1294, 1993.
  10. I. Singh, “A Study of Effective Segmentation Techniques for Liver Segmentation,” vol. 4, no. 4, pp. 1661–1666, 2015.
  11. E. S. Bialecki and A. M. Di Bisceglie, “Diagnosis of hepatocellular carcinoma,” Hpb, vol. 7, no. 1, pp. 26–34, 2005.
  12. Y. Sumida, A. Nakajima, and Y. Itoh, “Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis,” World J. Gastroenterol., vol. 20, no. 2, pp. 475–485, 2014.
  13. D. C. Rockey, S. H. Caldwell, Z. D. Goodman, R. C. Nelson, and A. D. Smith, “Liver biopsy,” Hepatology, vol. 49, no. 3, pp. 1017–1044, 2009.
  14. L.-K. Lee and S.-C. Liew, “A survey of medical image processing tools,” 2015 4th Int. Conf. Softw. Eng. Comput. Syst., no. October, pp. 171–176, 2015.
  15. T. T. Tran, J. Ahn, and N. S. Reau, “ACG Clinical Guideline: Liver Disease and Pregnancy,” Am. J. Gastroenterol., vol. 111, no. 2, pp. 176–194, 2016.
  16. American College of Obstetricians and Gynecologists’ Committee on Obstetric Practice, “Committee Opinion No. 656: Guidelines for Diagnostic Imaging During Pregnancy and Lactation.,” Obstet. Gynecol., vol. 127, no. 2, pp. e75-80, 2016.
  17. National Intitute of Diabetes and Digestive and Kidney Diseases, “What is cirrhosis?,” NIH Publ., vol. No. 14–113, pp. 1–16, 2014.
  18. C. Guy and D. Ffytche, An introduction to the principles of medical imaging, vol. 1542, no. 9. 2015.
  19. L. Wang, T. Chitiboi, H. Meine, M. G??nther, and H. K. Hahn, “Principles and methods for automatic and semi-automatic tissue segmentation in MRI data,” Magn. Reson. Mater. Physics, Biol. Med., vol. 29, no. 2, pp. 95–110, 2016.
  20. T. Heimann et al., “Comparison and evaluation of methods for liver segmentation from CT datasets,” IEEE Trans. Med. Imaging, vol. 28, no. 8, pp. 1251–1265, 2009.
  21. A. Bereciartua, A. Picon, A. Galdran, and P. Iriondo, “Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization,” Biomed. Signal Process. Control, vol. 20, pp. 71–77, 2015.
  22. C. Platero et al., “CHARACTERISATION.”
  23. O. Gloger, J. K??hn, A. Stanski, H. V??lzke, and R. Puls, “A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images,” Magn. Reson. Imaging, vol. 28, no. 6, pp. 882–897, 2010.
  24. F. López-Mir, V. Naranjo, J. Angulo, M. Alca˜niz and L. Luna, “Liver segmentation in MRI: A fully automatic method based on stochastic partitions,” Comput. Methods Programs Biomed., vol. 114, no. 1, pp. 11–28, 2014.
  25. D. Chi, Y. Zhao, and M. Li, “Automatic liver MR image segmentation with self-organizing map and hierarchical agglomerative clustering method,” Proc. - 2010 3rd Int. Congr. Image Signal Process. CISP 2010, vol. 3, pp. 1333–1337, 2010.
  26. D. Seghers et al., “Landmark based liver segmentation using local shape and local intensity models,” Proc. Work. 10th Int. Conf. MICCAI, Work. 3D Segmentation Clin. A Gd. Chall., pp. 135–142, 2007.
  27. I. Singh, “Optimized Liver Segmentation using Ant Colony Optimization,” vol. 4, no. 9, pp. 2434–2439, 2015.
  28. N. H. Abdel-massieh, M. M. Hadhoud, and K. A. Moustafa, “A fully automatic and efficient technique for liver segmentation from abdominal CT images,” Informatics Syst. (INFOS), 2010 7th Int. Conf., pp. 1–8, 2010.
  29. W. Nural, J. Hj, and H. Burkhardt, “Automatic 3D Liver Segmentation Using Morphological Operations and Graph-Cut T echniques,” pp. 23–34, 2011.
  30. P. Kar and R. Jain, “Imaging of Space Occupying Lesions of Liver,” 2011.
  31. Onwuchekwa R.C, “Radiological anatomy of the liver.,” Journal of Medicine and Medical Sciences. Vol. 7(4) pp. 072-078, July 2016.
  32. A. P. Wasnik, M. B. Mazza, U. R. Lalchandani, and P. S. Liu, “Normal and Variant Abdominal Anatomy on Magnetic Resonance Imaging,” Magn. Reson. Imaging Clin. N. Am., vol. 19, no. 3, pp. 521–545, 2011.
  33. A. A. M. Al-shammaa and H. R. Mohamed “Extraction of connected components Skin pemphigus diseases image edge detection by Morphological operations,” International Journal of Computer Applications , vol. 46, no. 18, pp. 7–13, 2012.
  34. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision, vol. 1, no. 4. pp. 321–331, 1988.
  35. R. Oak, “A Study of Digital Image Segmentation Techniques,” Int. J. Eng. Comput. Sci., vol. 5, no. 12, pp. 19779–19783, 2016.
  36. M. Bakoš, “Active Contours and their Utilization at Image Segmentation,” 5th Slovakian-Hungarian Jt. Symp. Appl. Mach. Intell. informatics, Poprad, Slovakia, pp. 313–317, 2007.
  37. S. Kemal and B. Acar, “Active Contours : A Brief Review,” 2006.
  38. K. Wang and C. Ma, “A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions,” Biomed. Eng. Online, vol. 15, no. 1, p. 39, 2016.
  39. A. Aly, S. Deris, and N. Zaki, “Research review for digital image Segmentation techniques,” Int. J. Comput. Sci., vol. 3, no. 5, pp. 99–106, 2011.
  40. P. Getreuer, “Chan-Vese Segmentation,” Image Process. Line, vol. 2, pp. 214–224, 2012.
  41. T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process., vol. 10, no. 2, pp. 266–277, 2001.
  42. T. Chan and L. Vese, “An Active Contour Model without Edges,” IEEE Trans.Image Process, vol. 10,no. 2, 2001
  43. R. Goldenberg, R. Kimmel, E. Rivlin, and M. Rudzsky, “Fast Geodesic Active Contours,” vol. 10, no. 10, pp. 1467–1475, 2001.
  44. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” IEEE Int. Conf. Comput. Vis., vol. 22, no. 1, pp. 694–699, 1995.
  45. M. Airouche, L. Bentabet, and M. Zelmat, “Image Segmentation Using Active Contour Model and Level Set Method Applied to Detect Oil Spills,” Proc. World Congr. Eng., vol. 1, no. 1, pp. 1–3, 2009.
  46. Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas and Dorin Comaniciu., “Automatic Liver Segmentation Using an Adversarial Image-to-Image Network,” 2017.
Index Terms

Computer Science
Information Sciences

Keywords

Medical Image Analysis Automatic Liver Segmentation Active Contour Model