Skip to main content
SpringerLink
Log in
Book cover

Abdomen and Thoracic Imaging pp 279–315Cite as

Shape-Based Liver Segmentation Without Prior Statistical Models

  • Chapter
  • First Online:

  • 1802 Accesses

Abstract

In this work, we introduce a shape-based liver segmentation approach. However, unlike the other shape-based approaches, this approach is model-free, and it does not require prior shape or intensity model construction. In contrary, we exploit the relation between consequent slices in multi-slice CT images to estimate and propagate shape and intensity constrains. Then, these constrains are integrated with a shape-based graph cut algorithm to extract the liver object in each slice. This approach needs a simple user interaction and it eliminates the burdens associated with model building like data collection, manual segmentation, registration, and landmark correspondence. Moreover, it is talented to deal with complex shape and intensity variations. This model-free approach was evaluated on 50 CT images from three different datasets with several liver abnormalities, including tumors and cysts, and it achieved high average gauged scores of 80.4, 79.2, and 81.7 for these datasets.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) GLOBOCAN 2008 v1.2, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 10 [Internet]. International Agency for Research on Cancer, Lyon, France. http://globocan.iarc.fr. Accessed 15/03/2013

  2. Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 75:S3–S19

    Article  Google Scholar 

  3. Fujita H, Zhang1 X, Kido S, Hara T, Zhou X, Hatanaka Y, Xu R (2010) An introduction and survey of computer-aided detection/diagnosis (CAD). In: 2010 international conference on future computer, control and communication (FCCC2010), pp 200–205

    Google Scholar 

  4. Lim SJ, Jeong YY, Ho YS (2006) Automatic liver segmentation for volume measurement in CT images. J Vis Commun Image Represent 17:860–875

    Article  Google Scholar 

  5. Rusko L, Bekes G, Fidrich M (2009) Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images. Med Image Anal 13:871–882

    Article  PubMed  Google Scholar 

  6. Chen Y, Wang Z, Zhao W, Yang X (2009) Liver segmentation from CT images based on region growing method. In: Proc. 3rd international conference on bioinformatics and biomedical engineering (ICBBE), pp 1–4

    Google Scholar 

  7. Beichel R, Bauer C, Bornik A, Sorantin E, Bischof H (2007) Liver segmentation in CT data: a segmentation refinement approach. In Proc. of MICCAI 2007 Workshop: 3D Segmentation in the Clinic-A Grand Challenge. pp 235–245

    Google Scholar 

  8. Beck A, Aurich V (2007) HepaTux A semiautomatic liver segmentation system. In Proc. of MICCAI 2007 Workshop: 3D Segmentation in the Clinic-A Grand Challenge. pp 225–233

    Google Scholar 

  9. Foruzana AH, Zoroofia RA, Horib M, Satoc Y (2009) A knowledge-based technique for liver segmentation in CT data. Comput Med Imaging Graph 33:567–587

    Article  Google Scholar 

  10. Foruzana AH, Zoroofia RA, Horib M, Satoc Y (2009) Liver segmentation by intensity analysis and anatomical information in multi-slice CT images. Int J Comput Assist Radiol Surg 4(3):287–297

    Article  Google Scholar 

  11. Gambino O, Vitabile S, Re GL, Tona GL, Librizzi S, Pirrone R, Ardizzone E, Midiri M (2010) Automatic volumetric liver segmentation using texture based region growing. In: Proc. of international conference on complex, intelligent and software intensive systems, pp 146–152

    Google Scholar 

  12. Susomboon R, Raicu DS, Furst J (2007) A hybrid approach for liver segmentation. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 151–160

    Google Scholar 

  13. Tibamoso G, Rueda A (2009) Semi-automatic liver segmentation from computed tomography (CT) scans based on deformable surfaces. SLIVER07 Results [Online]. http://sliver07.isi.uu.nl/results/20091022201318/description.pdf

  14. Masutani Y (2002) RBF-based representation of volumetric data: application in visualization and segmentation. In: Proc. medical image computing and computer-assisted intervention (MICCAI), pp 300–307

    Google Scholar 

  15. Wimmer A, Soza G, Hornegger J (2007) Two-stage semi-automatic organ segmentation framework using radial basis functions and level sets. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 179–188

    Google Scholar 

  16. Gao J, Kosaka A, Kak A (2005) A deformable model for automatic CT liver extraction. Acad Radiol 12(9):1178–1189

    Article  PubMed  Google Scholar 

  17. Rahardja K, Kosaka A (1996) Vision-based bin picking: recognition and localization of multiple complex objects using simple visual cues. In: Proc. IEEE/RSJ international conference on intelligent robots and systems, pp 1448–1457

    Google Scholar 

  18. Massoptier L, Casciaro S (2008) A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol 18:1658–1665

    Article  PubMed  Google Scholar 

  19. Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  PubMed  CAS  Google Scholar 

  20. Alomari RS, Kompalli S, Chaudhary V (2008) Segmentation of the liver from abdominal CT using Markov random field model and GVF snakes. In: Proc. international conference on complex, intelligent and software intensive systems, pp 293–298

    Google Scholar 

  21. Liu F, Zhao B, Kijewski P, Ginsberg MS, Wang L, Schwartz LH (2004) Automatic liver contour segmentation using GVF snake. In: Proc. SPIE medical imaging: image processing, vol 5370, pp 1466–1473

    Google Scholar 

  22. Liu F, Zhao B, Kijewski PK, Wang L, Schwartz LH (2005) Liver segmentation for CT images using GVF snake. Med Phys 32(12):3699–3706

    Article  PubMed  Google Scholar 

  23. Sethian JA (1996) Level set methods and fast marching methods, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  24. Furukawa D, Shimizu A, Kobatake H (2007) Automatic liver segmentation method based on maximum a posterior probability estimation and level set method. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 117–124

    Google Scholar 

  25. Manuel LF, Rubio JL, Ledesma-Carbayo MJ, Pascau J, Tellado JM, Ramn E, Desco M, Santos A (2009) 3D liver segmentation in preoperative CT images using a level-sets active surface method. In: Proc. 31st annual international conference of the IEEE EMBS, pp 3625–3628

    Google Scholar 

  26. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  PubMed  CAS  Google Scholar 

  27. Lee J, Kim N, Lee H, Seo JB, Won HJ, Shin YM, Shin YG (2007) Efficient liver segmentation exploiting level-set speed images with 2.5D shape propagation. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 189–196

    Google Scholar 

  28. Freiman M, Eliassaf O, Taieb Y, Joskowicz L, Azraq Y, Sosna J (2008) An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation. Int J Comput Assist Radiol Surg 3(5):439–446

    Article  Google Scholar 

  29. Kainmuller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 109–116

    Google Scholar 

  30. Heimann T, Meinzer H, Wolf I (2007) A statistical deformable model for the segmentation of liver CT volumes. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 161–166

    Google Scholar 

  31. Schwefel H-P (1995) Evolution and optimum seeking. Wiley, New York

    Google Scholar 

  32. Afifi A, Nakaguchi T, Tsumura N, Miyake Y (2010) Shape and texture priors for liver segmentation in abdominal computed tomography scans using the particle swarm optimization algorithm. Med Imaging Technol 28(1):53–62

    Google Scholar 

  33. Afifi A, Nakaguchi T, Tsumura N, Miyake Y (2010) A model optimization approach to the automatic segmentation of medical images. IEICE Trans Inform Syst E93-D(4):882–889

    Article  Google Scholar 

  34. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1:33–57

    Article  Google Scholar 

  35. Saddi KA, Rousson M, Chefd’hotel C, Cheriet F (2007) Global-to-local shape matching for liver segmentation in CT imaging. In: Proc. MICCAI workshop on 3-D segmentation in clinic: a grand challenge, pp 207–2014

    Google Scholar 

  36. Lamecker H, Lange T, Seebass M (2004) Segmentation of the liver using a 3D statistical shape model. Technical report, Zuse Institute, Berlin, Germany

    Google Scholar 

  37. Okada T, Shimada R, Hori M, Nakamoto M, Chen Y-W, Nakamura H, Sato Y (2008) Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. Acad Radiol 15(11):1390–1399

    Article  PubMed  Google Scholar 

  38. Linguraru MG, Li Z, Shah F, Chin S, Summers RM (2009) Automated liver segmentation using a normalized probabilistic atlas. In: Proc. SPIE medical imaging: biomedical applications in molecular, structural, and functional imaging, vol 7262, pp 72622R72622R-8

    Google Scholar 

  39. Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492

    Article  PubMed  Google Scholar 

  40. Linguraru MG, Pura JA, Chowdhury AS, Summers RM (2010) Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization. Medical image computing and computer-assisted intervention MICCAI2010, lecture notes in computer science, vol 6363

    Google Scholar 

  41. Bidaut L (2000) Data and image processing for abdominal imaging. Abdom Imaging 25:341–360

    Article  PubMed  CAS  Google Scholar 

  42. Weickert J (1997) A review of nonlinear diffusion filtering. Scale-Space Theory Comput Vis 1552:1–28

    Article  Google Scholar 

  43. Lamecker H, Lange T, Seebass M (2004) Segmentation of the liver using a 3D statistical shape model. ZIB-Report 04–09 [Online]. http://opus.kobv.de/zib/volltexte/2004/785/pdf/ZR04-09.pdf

  44. Weickert J, Romeny BM, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7(3):398–410

    Article  PubMed  CAS  Google Scholar 

  45. de Boor C (1978) A practical guide to splines. Springer, New York

    Book  Google Scholar 

  46. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239

    Article  Google Scholar 

  47. Griffin LD, Colchester ACF, Roll SA, Studholme CS (1994) Hierarchical segmentation satisfying constraints. In: Proc. of British machine vision conference (BMVC94), pp 135–144

    Google Scholar 

  48. Esneault S, Lafon C (2010) Liver vessels segmentation using a hybrid geometrical moments/graph cuts method. IEEE Trans Biomed Eng 57(2):276–283

    Article  PubMed  Google Scholar 

  49. Boykov Y, Lea GF (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vis 70(2):109–131

    Article  Google Scholar 

  50. Nagahashi T, Fujiyoshi H, Kanade T (2007) Image segmentation using iterated graph cuts based on multi-scale smoothing. In: Proc. 8th Asian conference on computer vision (ACCV), part II, pp 806–816

    Google Scholar 

  51. Boykov Y, Jolly M-P (2000) Interactive organ segmentation using graph cuts. In: Proc. medical image computing and computer-assisted intervention (MICCAI), pp 276–286

    Google Scholar 

  52. Freedman D, Zhang T (2005) Interactive graph cut based segmentation with shape priors. In: Proc. IEEE computer society conference on computer vision and pattern recognition (CVPR), vol 1, pp 755–762

    Google Scholar 

  53. Segmentation of the Liver 2007 (SLIVER07). http://sliver07.isi.uu.nl/. Last visited: Accessed 10 Apr 2013

  54. The Japanese Society of Medical Imaging Technology, JAMIT Computer-aided Diagnosis (CAD). http://www.jamit.jp/english/. Overview, last visited: Accessed 10 Apr 2013

  55. Heimann T, Ginneken BV, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmu´ller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer H-P, Nemeth G, Raicu DS, Rau A-M, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Menofia University, Shibin Al-Kawm, Menofia, Egypt

    Ahmed Afifi

  2. Graduate School of Engineering, Chiba University, Japan, 1-33, Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan

    Toshiya Nakaguchi

Authors
  1. Ahmed Afifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Toshiya Nakaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Afifi .

Editor information

Editors and Affiliations

  1. Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA

    Ayman S. El-Baz

  2. University of Cagliari, Cagliari, Italy

    Luca Saba

  3. Biomedical Technologies, Inc Idaho State University (Affiliated), Roseville, California, USA

    Jasjit Suri

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Afifi, A., Nakaguchi, T. (2014). Shape-Based Liver Segmentation Without Prior Statistical Models. In: El-Baz, A., Saba, L., Suri, J. (eds) Abdomen and Thoracic Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8498-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-8498-1_11

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-8497-4

  • Online ISBN: 978-1-4614-8498-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation