Abstract
The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images, recent methods presented in the literature to obtain liver segmentation are viewed. Generally, liver segmentation methods are divided into two main classes, semi-automatic and fully automatic methods, under each of these two categories, several methods, approaches, related issues and problems will be defined and explained. The evaluation measurements and scoring for the liver segmentation are shown, followed by the comparative study for liver segmentation methods, pros and cons of methods will be accentuated carefully. In this paper, we concluded that automatic liver segmentation using CT images is still an open problem since various weaknesses and drawbacks of the proposed methods can still be addressed.
Similar content being viewed by others
References
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
Barrett W, Mortensen EN (1997) Interactive live-wire boundary extraction. Med Imaging Anal 1(4): 331–341
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, pp 225–234
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
Boykov Y, Funka-Lea G (2006) Graph cuts and efficient n-d image segmentation. IJCV 70(2): 109–131
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
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
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
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
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1): 21–27
Dawant BM, Li R, Lennon B, Li S (2007) Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 215–221
Duda RO, Hart PE, Stork DG (2000) Pattern classification. 2nd edn. Wiley interscience, New York
Foruzan AH, Aghaeizadeh ZR, Hori M, Sato 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
Gao L, Heath DG, Fishman EK (1998) Abdominal image segmentation using three-dimensional deformal models. Investiga Radiol 33(6):348–355
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6): 610–621
Heimann T et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8): 1251–1265
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
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
Lamecker H, Lange R, Seeba M (2004) Segmentation of the liver using a 3d statistical shape model. Technical report Zuse Institue, Berlin, pp, pp 1–25
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
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
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
Lim SJ, Jeong YY, Ho YS (2006) Automatic liver segmentation for volume measurement in CT Images. JVCIR 17(4): 860–875
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
Maes F, Collignon A, Vandermeulen D, Suetens GMP (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2): 187–198
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
McLachlan GJ, Krishnan T (2009) The EM algorithm and extensions, 2nd edn. Wiley-Interscience, Hoboken
Montagnat J, Delingette H (1996) Volumetric medical images segmentation using shape constrained deformable models. In: Proceedings of CVRMed-MRCAS, pp 13–22
Pan S, Dawant BM (2001) Automatic 3-D segmentation of the liver from abdominal CT images: a level-set approach. In: Proceedings of SPIE on medical imaging, pp 128–138
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
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
Rohlfing T, Brandt R, Menzel R, Russakoff DB, Maurer CR (2005) Quo vadis, atlas-based segmentation? Handbook of medical image analysis—Volume III: Registration models. Kluwer Academic, Norwell MA, pp, pp 435–486
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
Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8): 712–721
Ruskó L, Bekes G, Németh G, Fidrich M (2007) Fully automatic liver segmentation for contrast- enhanced CT images. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 143–150
Saddi KA, Rousson M, Chefd’hotel C, Cheriet F (2007) Global-to-local shape matching for liver segmentation in CT imaging. In. Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 207–214
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
Seo KS, Park JA (2005) Improved automatic liver segmentation of a contrast enhanced CT image. Advances in multimedia information process—PCM, pp 899–909
Slagmolen P, Elen A, Seghers D, Loeckx D, Maes F, Haustermans, K (2007) Atlas based liver segmentation using nonrigid registration with a B-spline transformation model. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 197–206
Soler L, Delingette H, Malandain G, Montagnat J, Ayache N, Koehl C, Dourthe O, Malassagne B, Smith M, 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
Sonka M, Hlavac V, Boyle R (2007) Mathematical morphology in image processing,analysis, and machine vision. Thomson, Newyork
Susomboon R, Raicu DS, Furst J (2007) A hybrid approach for liver segmentation. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 151–160
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
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 Trans Med Imaging 22(2): 137–154
Weickert J, Romeny BMTH, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Imaging Process 7(3): 398–410
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mharib, A.M., Ramli, A.R., Mashohor, S. et al. Survey on liver CT image segmentation methods. Artif Intell Rev 37, 83–95 (2012). https://doi.org/10.1007/s10462-011-9220-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-011-9220-3