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.
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References
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
Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 75:S3–S19
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
Lim SJ, Jeong YY, Ho YS (2006) Automatic liver segmentation for volume measurement in CT images. J Vis Commun Image Represent 17:860–875
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
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
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
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
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
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
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
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
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
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
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
Gao J, Kosaka A, Kak A (2005) A deformable model for automatic CT liver extraction. Acad Radiol 12(9):1178–1189
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
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
Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369
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
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
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
Sethian JA (1996) Level set methods and fast marching methods, 2nd edn. Cambridge University Press, Cambridge
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
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
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
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
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
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
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
Schwefel H-P (1995) Evolution and optimum seeking. Wiley, New York
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
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
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1:33–57
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
Lamecker H, Lange T, Seebass M (2004) Segmentation of the liver using a 3D statistical shape model. Technical report, Zuse Institute, Berlin, Germany
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
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
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
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
Bidaut L (2000) Data and image processing for abdominal imaging. Abdom Imaging 25:341–360
Weickert J (1997) A review of nonlinear diffusion filtering. Scale-Space Theory Comput Vis 1552:1–28
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
Weickert J, Romeny BM, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7(3):398–410
de Boor C (1978) A practical guide to splines. Springer, New York
Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239
Griffin LD, Colchester ACF, Roll SA, Studholme CS (1994) Hierarchical segmentation satisfying constraints. In: Proc. of British machine vision conference (BMVC94), pp 135–144
Esneault S, Lafon C (2010) Liver vessels segmentation using a hybrid geometrical moments/graph cuts method. IEEE Trans Biomed Eng 57(2):276–283
Boykov Y, Lea GF (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vis 70(2):109–131
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
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
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
Segmentation of the Liver 2007 (SLIVER07). http://sliver07.isi.uu.nl/. Last visited: Accessed 10 Apr 2013
The Japanese Society of Medical Imaging Technology, JAMIT Computer-aided Diagnosis (CAD). http://www.jamit.jp/english/. Overview, last visited: Accessed 10 Apr 2013
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
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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
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