Survey on Liver Segmentation Schemes in CT Images

Authors

  • Sakshi Thapa  Shri Mata Vaishno Devi University, Kakryal, Katra, J&K, , India
  • Baij Nath Kaushik   Shri Mata Vaishno Devi University, Kakryal, Katra, J&K, , India

Keywords:

Abstract

In the field of medical image processing, the segmentation of liver in computed tomography images are of enormous significance. Dividing schemes into two categories that are semi-automatic and fully automatic schemes. Both classes have some techniques, approximation, related queries; some drawbacks will be described and clarified. To obtain a liver segmentation, there is an analysis on methods for segmentation of liver as well as techniques using computed tomography images are shown. Following the relative study for liver segmentation schemes various measurements, scoring for liver segmentation are given; advantages and disadvantages of techniques will be emphasized carefully. Several faults and difficulties of the suggested methods are still to be focused.

References

  1. Arya S, Mount DM, Netanyahu NS, Silverman R, Wu A (1998) an optimal algorithm for approximate nearest neighbor searching. J ACM 45(6):891–923
  2. Barrett W, Mortensen EN (1997) Interactive live-wire boundary extraction. Med Imaging Anal 1(4):331–341
  3. Beck A, Aurich V (2007) HepaTux-a semiautomatic liver segmentation system. In: Proceedings of MICCAI Workshop on 3D segmentation in the clinic: a grand challenge pp225-234
  4. Beichel R, Bauer C, Bornik A, Sorantin E, Bischof H (2007) Liver segmentation in CT data: a segmentation refinement approach. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand Challenge, pp 235–245
  5. Campadelli P, Casiraghi E, Esposito A (2009) Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med 45(2–3):185–196
  6. Carr JC, Beatson RK, Cherrie JB, Mitchell TJ, Fright WR, McCallum BC, Evans TR (2001) Reconstruction and representation of 3-D objects with radial basis functions. In: Proceedings of SIGGRAPH, pp 67–76
  7. Chi Y, Cashman PMM, Bello F, Kitney RI,(2007) A discussion on the evaluation of a new automatic liver volume segmentation method for specified CT image datasets. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 167–175
  8. Cootes TF, Hill A, Taylor CJ, Haslam J (1994) Use of active shape models for locating structures in medical images. Imag Vis Comput 12(6):355–366
  9. Heimann T et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265
  10. Kainmüller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 109–116
  11. Koss JE,Newman FD, Johnson TK,Kirch DL (1999) Abdominal organ segmentation using texture transforms and a hopfield neural network. IEEE Trans Med Imaging 18(7):640–648
  12. Lamecker H, Lange R, SeebaM (2004) Segmentation of the liver using a 3d statistical shape model. Technical report Zuse Institue, Berlin, pp 1–25
  13. Lee CC, Chung PC, Tsa H (2003) Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules. IEEE Trans Inf Technol Biomed 7(3):208–217
  14. Lim SJ, Jeong, YY, Ho YS (2004) Automatic segmentation of the liver in ct images using the watershed algorithm based on morphological filtering. In: Proceedings of SPIE, pp 1658–1666
  15. Lim SJ, Jeong, YY, Ho YS (2005) Segmentation of the liver using the deformable contour method on CT images. In: Proceedings of SPIE medical imaging, pp 570–581
  16. Lim SJ, Jeong YY, Ho YS (2006) Automatic liver segmentation for volume measurement in CT Images. JVCIR 17(4):860–875
  17. Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22(1):120–128
  18. Montagnat J, Delingette H (1996) Volumetric medical images segmentation using shape constrained deformable models. In: Proceedings of CVRMed-MRCAS, pp 13–22
  19. Pil UK, Yun JL, Youngjin J, Jin HC, Myoung NK, (2006) Liver extraction in the abdominal CT image by watershed segmentation algorithm. World congress of medical physics and biomedical engineering, pp 2563–2566
  20. Rikxoort E, Arzhaeva Y, Ginneken B (2007) Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 101–108
  21. Rohlfing T, Brandt R, Menzel R, Russakoff DB, Maurer CR (2005) Quo vadis, atlas-based segmentation?
  22. Handbook of medical image analysis—Volume III: Registration models. Kluwer Academic, Norwell MA, pp 435–486
  23. Rousson M, Cremers D (2005) Efficient kernel density estimation of shape and intensity priors for level set segmentation. In: Proceedings of MICCAI, pp 757–764
  24. RueckertD, Sonoda LI,Hayes C, Hill DL,LeachMO,HawkesDJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721
  25. Schenk A, Prause GP, Peitgen H (2001) Local cost computation for efficient segmentation of 3d objects with live wire. In: Proceedings of SPIE on medical imaging, pp 1357–1364
  26. Seo KS, Park JA (2005) Improved automatic liver segmentation of a contrast enhanced CT image. Advances in multimedia information process—PCM, pp 899–909
  27. Slagmolen P, Elen A, Seghers D, Loeckx D, Maes F, Haustermans, K (2007) Atlas based liver segmentation using no rigid registration with a B-spline transformation model. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 197–206
  28. Soler L, Delingette H, Malandain G, Montagnat J, Ayache N, Koehl C, Dourthe O, Malassagne B, Smith M,
  29. Mutter D, Marescaux J (2001) Fully automatic anatomical, pathological, and functional segmentation from ct scans for hepatic surgery. Comput Aided Surg 6(3):131–142
  30. Sonka M, Hlavac V, Boyle R (2007) Mathematical morphology in image processing, analysis, and machine vision. Thomson, Newyork
  31. Susomboon R, Raicu DS, Furst J (2007) Ahybrid approach for liver segmentation. In: Proceedings of MICCAI Workshop on 3D segmentation in the clinic: a grand challenge, pp 151–160
  32. Tsai D, Tanahashi N (1994) Neural-network-based boundary detection of liver structure in ct images for 3-d visualization. In: Proceedings of IEEE international conference neural networks, pp 3484–3489
  33. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson W, Willsky A (2003) A shape- based approach to the segmentation of medical imagery using level sets. IEEE TransMed Imaging 22(2):137–154
  34. Weickert J, Romeny BMTH, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Imaging Process 7(3):398–410
  35. Wimmer A, Soza G, Hornegger J (2007) Two-stage semi-automatic organ segmentation framework using radial basis functions and level sets: In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 179–188

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-09-30

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Volume 2, Issue 7, September-2017

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Research Articles

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[1]
Sakshi Thapa, Baij Nath Kaushik , " Survey on Liver Segmentation Schemes in CT Images" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.219-225, September-2017.