A likelihood and local constraint level set model for liver tumor segmentation from CT volumes

IEEE Transactions on Biomedical Engineering

Volumn 60, Issue 10, 2013, Pages 2967-2977

  Li, Changyang ^a   Wang, Xiuying ^b   Eberl, Stefan ^c,d   Fulham, Michael ^c,d   Yin, Yong ^e   Chen, Jinhu ^e   Feng, David Dagan ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

^b UNIVERSITY OF SYDNEY   (Australia)

^c UNIVERSITY OF SYDNEY   (Australia)

^d ROYAL PRINCE ALFRED HOSPITAL   (Australia)

^e SHANDONG CANCER HOSPITAL AND INSTITUTE   (China)

Author keywords

Computed tomography (CT); edge detector; level set function; likelihood; liver hepatic tumor segmentation

Indexed keywords

DENSITY DISTRIBUTIONS; EDGE DETECTORS; LEVEL SET FUNCTIONS; LIKELIHOOD; LIVER TUMOR SEGMENTATIONS; MANUAL SEGMENTATION; TUMOR SEGMENTATION; VOLUME DIFFERENCE;

GEODESY; TUMORS;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; DENSITY; DIAGNOSTIC ACCURACY; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; LIVER TUMOR; PARAMETERS; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; VALIDATION PROCESS;

CARCINOMA, HEPATOCELLULAR; COMPUTER SIMULATION; HUMANS; LIKELIHOOD FUNCTIONS; LIVER NEOPLASMS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED; TUMOR BURDEN;

EID: 84884545326     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2013.2267212     Document Type: Article

References (42)

1. WHO, World Health Statistics 2008. Geneva, Switzerland: WHO Press, 2008.
2. M. Parkin, F. Bray, J. Ferlay, and P. Pisani, "Global cancer statistics, 2002," CA-Cancer J. Clin., vol. 55, pp. 74-108, 2005. (Pubitemid 40372904)
3. WHO. The world health report 2004 - Changing history. [Online]. Available: http://www.who.int/whr/2004/en/2004
4. F. Conversano, R. Franchini, C. Demitri, L. Massoptier, F. Montagna, A. Maffezzoli, A. Malvasi, and S. Casciaro, "Hepatic vessel segmentation for 3-D planning of liver surgery: Experimental evaluation of a new fully automatic algorithm," Acad. Radiol., vol. 18, pp. 461-470, 2011.
5. P. Lamata, F. Lamata, V. Sojar, P. Makowski, L. Massoptier, S. Casciaro, W. Ali, T. Studeli, J. Declerck, and O. J. Elle, "Use of the Resection Map system as guidance during hepatectomy," Surg. Endosc., vol. 24, pp. 2327-2337, 2010.
6. A. Mahr, S. Levegrun, M. L. Bahner, J. Kress, I. Zuna, and W. Schlegel, "Usability of semi-automatic segmentation algorithms for tumor volume determination," Invest. Radiol., vol. 34, pp. 143-150, 1999. (Pubitemid 29110716)
7. R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 6, pp. 641-647, Jun. 1994.
8. B. Zhao and D. Yankelevitz, "Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images," Med. Phys., vol. 26, no. 6, pp. 4678-4689, 1999.
9. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," Int. J. Comput. Vis., vol. 22, pp. 61-79, 1997. (Pubitemid 127507390)
10. D. Wong, J. Liu, Y. Fengshou, Q. Tian, W. Xiong, J. Zhou, Y. Qi, T. Han, and S. K. Venkatesh, "A semi-automated method for liver tumor segmentationbased on 2D region growing with knowledge-based constraints," in Proc. MICCAI Workshop 3-D Segmentat.Clin.: A Grand Challenge II, 2008. Available: http://www.grand-challenge2008.bigr.nl/proceedings/liver/articles. html
11. Y. Qi, W. Xiong, W. K. Leow, Q. Tian, J. Zhou, J. Liu, T. Han, S. K. Venkatesh, and S.-C. Wang, "Semi-automatic segmentation of liver tumors from CT scans using Bayesian rule-based 3D region growing," in Proc. MICCAI Workshop 3-D Segmentation Clin.: A Grand Challenge II, 2008. Available: http://www.grand-challenge2008.bigr.nl/proceedings/liver/articles.html
12. C. Xu and J. L. Prince, "Snakes, shapes, and gradient vector flow," IEEE Trans. Image Process., vol. 7, no. 3, pp. 359-369, Mar. 1998. (Pubitemid 128745354)
13. L. Massoptier and S. Casciaro, "A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans," Eur. Radiol., vol. 18, pp. 1658-1665, 2008.
14. S. Casciaro, R. Franchini, L. Massoptier, E. Casciaro, F. Conversano, A. Malvasi, andA. Lay-Ekuakille, "Fully automatic segmentations of liver and hepatic tumors from 3-D computed tomography abdominal images: Comparative evaluation of two automatic methods," IEEE Sens. J., vol. 12, pp. 464-473, 2012.
15. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/max- flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124-1137, Sep. 2004.
16. M. Freiman, O. Cooper, D. Lischinski, and L. Joskowicz, "Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation," Int. J. Comput. Assist. Radiol. Surg., vol. 6, pp. 247-255, 2011.
17. Y. Taieb, O. Eliassaf, M. Freiman, L. Joskowicz, and J. Sosna, "An iterative Bayesian approach for liver analysis: tumors validation study," in Proc. MICCAI Workshop 3-D Segmentation Clin.: A Grand Challenge II, 2008. Available: http://www.grand-challenge2008. bigr.nl/proceedings/liver/articles. html
18. R. Beichel, A. Bornik, C. Bauer, and E. Sorantin, "Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3-D segmentation refinement methods," Med. Phys., vol. 39, pp. 1361-1373, 2012.
19. D. Smeets, D. Loeckx, B. Stijnen, B. De Dobbelaer, D. Vandermeulen, and P. Suetens, "Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification," Med. Image Anal., vol. 14, pp. 13-20, 2010.
20. P. J. Yim and D. J. Foran, "Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms," in Proc. 16th IEEE Symp. Comput.-Based Med. Syst., 2003, pp. 329-335.
21. S. Osher and J. Sethian, "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi Formulations," J. Comput. Phys., vol. 79, pp. 12-49, Nov. 1988.
22. V. Caselles, F. Catte, T. Coll, and F. Dibos, "A geometric model for active contours in image processing," Numer. Math., vol. 66, pp. 1-31, 1993.
23. T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Trans. Image Process, vol. 10, no. 2, pp. 266-277, Feb. 2001. (Pubitemid 32254596)
24. R. Kimmel, A. Amir, and A. M. Bruckstein, "Finding shortest paths on surfaces using level sets propagation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 6, pp. 635-640, Jun. 1995.
25. R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: A level set approach," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 2, pp. 158-175, Feb. 1995.
26. A. Vasilevskiy and K. Siddiqi, "Flux maximizing geometric flows," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1565-1578, Dec. 2002.
27. A. Tsai, A. Yezzi, and A. S.Willsky, "Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification," IEEE Trans. Image Process., vol. 10, no. 8, pp. 1169-1186, Aug. 2001. (Pubitemid 32732678)
28. M. Rousson and R. Deriche, "A variational framework for active and adaptative segmentation of vector valued images," in Proc. Workshop Motion Video Comput., 2002, pp. 56-61.
29. L. Vese and T. Chan, "A multiphase level set framework for image segmentation using the Mumford and Shah model," Int. J. Comput. Vis., vol. 50, pp. 271-293, Dec. 2002. (Pubitemid 35377416)
30. C. Li, X. Wang, S. Eberl, M. Fulham, and D. Feng, "A robust model for segmenting images with/without intensity inhomogeneities," IEEE Trans. Image Process., vol. 22, no. 8, pp. 3296-3309, Aug. 2013.
31. B. Sandberg, S. H. Kang, and T. F. Chan, "Unsupervised multiphase segmentation: A phase balancing model," IEEE Trans. Image Process., vol. 19, no. 1, pp. 119-130, Jan. 2010.
32. S. Kim and M. Kang, "Multiple-region segmentation without supervision by adaptive global maximum clustering," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1600-1612, Apr. 2012.
33. A. Albert Huang, R. Abugharbieh, and R. Tam, "A hybrid geometric- statistical deformable model for automated 3-D segmentation in brain MRI," IEEE Trans. Biomed. Eng., vol. 56, no. 7, pp. 1838-1848, Jul. 2009.
34. G. Gilboa,N. Sochen, andY. Y. Zeevi, "Image enhancement and denoising by complex diffusion processes," IEEE Trans. Pattern Anal.Mach. Intell., vol. 26, no. 8, pp. 1020-1036, Aug. 2004.
35. A. Yezzi, Jr., S. Kichenassamy, A. Kumar, P. Olver, and A. Tannenbaum, "A geometric snake model for segmentation of medical imagery," IEEE Trans. Med. Imaging., vol. 16, no. 2, pp. 199-209, Apr. 1997. (Pubitemid 127762931)
36. C. Li, C. Xu, C. Gui, and M. D. Fox, "Distance regularized level set evolution and its application to image segmentation," IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243-3254, Dec. 2010.
37. T. K. Moon, "The expectation-maximization algorithm," IEEE Signal Process. Mag., vol. 13, no. 6, pp. 47-60, Nov. 1996. (Pubitemid 126518983)
38. G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and The Calculus of Variations, 2nd ed. vol. 147. New York, NY, USA: Springer-Verlag, 2006.
39. L. Soler, A. Hostettler, V. Agnus, A. Charnoz, J.-B. Fasquel, J. Moreau, A.-B. Osswald, M. Bouhadjar, and J. Marescaux. 3D Image reconstruction for comparison of algorithm database : A patientspecific anatomical and medical image database. [Online]. Available: http://www.ircad.fr/softwares/3Dircadb/ 3Dircadb.php?lng = en
40. T. Heimann, B. van Ginneken, and M. A. Styner, "Comparison and evaluation of methods for liver segmentation from CT datasets," IEEE Trans. Med. Imag., vol. 28, no. 8, pp. 1251-1265, Aug. 2009.
41. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu, "An optimal algorithm for approximate nearest neighbor searching fixed dimensions," J. ACM, vol. 45, pp. 891-923, 1998.
42. X. Deng and G. Du, "Editorial: 3D segmentation in the clinic: A grand challenge II - Liver tumor segmentation," in Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, 2008. Available: http://www.grand-challenge2008.bigr.nl/proceedings/liver/articles.html