research-article

An improved level set for liver segmentation and perfusion analysis in MRIs

Authors:

Jianrong XuAuthors Info & Claims

IEEE Transactions on Information Technology in Biomedicine, Volume 13, Issue 1

Pages 94 - 103

https://doi.org/10.1109/TITB.2008.2007110

Published: 01 January 2009 Publication History

Abstract

Determining liver segmentation accurately from MRIs is the primary and crucial step for any automated liver perfusion analysis, which provides important information about the blood supply to the liver. Although implicit contour extraction methods, such as level set methods (LSMs) and active contours, are often used to segment livers, the results are not always satisfactory due to the presence of artifacts and low-gradient response on the liver boundary. In this paper, we propose a multiple-initialization, multiple-step LSM to overcome the leakage and over-segmentation problems. The multiple-initialization curves are first evolved separately using the fast marching methods and LSMs, which are then combined with a convex hull algorithm to obtain a rough liver contour. Finally, the contour is evolved again using global level set smoothing to determine a precise liver boundary. Experimental results on 12 abdominal MRI series showed that the proposed approach obtained better liver segmentation results, so that a refined liver perfusion curve without respiration affection can be obtained by using a modified chamfer matching algorithm and the perfusion curve is evaluated by radiologists.

References

[1]

T. R. Bader, A. M. Herneth, W. Blaicher, R. Steininger, F. Muhlbacher, G. Lechner, and F. Grabenwoger, "Hepatic perfusion after liver transplantation: Noninvasive measurement with dynamic single-section CT," Radiology, vol. 209, pp. 129-134, 1998.

[2]

N. Sebastian and L. Gu, "A novel liver perfusion analysis method," in Proc. 27th Annu. Conf. IEEE Eng. Med. Biol., 2005, pp. 3063- 3066.

[3]

G. Chen and L. Gu, "A novel liver perfusion analysis based on active contours and chamfer matching," in Medical Imaging and Augmented Reality (Lecture Notes in Computer Science 4091). Berlin, Germany: Springer-Verlag, 2006, pp. 108-115.

[4]

J. A. Sethian, "Level set methods and fast marching methods: Evolving interfaces in computational geometry, fluid mechanics," in Computer Vision and Material Science. Cambridge, U.K.: Cambridge Univ. Press, 1999.

[5]

J. S. Suri, S. K. Setarehdan, and S. Singh, Advanced Algorithmic Approaches to Medical Image Segmentation: State-of the- Art Applications in Cardiology, Neurology, Mammography and Pathology. Berlin, Germany: Springer-Verlag, 2001.

[6]

R. Malladi and J. A. Sethian, "Image processing via level set curvature flow," Proc. Nat. Acad. Sci., vol. 92, no. 15, pp. 7046-7050, 1995.

[7]

L. Hua, E. Abderrahim, F. Jaral, and R. Su, "An improved image segmentation approach based on level set and mathematical morphology," in Proc. SPIE, 3rd Int. Symp. Multispectral Image Process. Pattern Recognit., Sep., 2003, vol. 5286, pp. 851-854.

[8]

A. Evans, T. Lambrou, A. Linnery, and A. Todd-Pokroped, "Automatic segmentation of liver using a topology adaptive snake," in Proc. Biomed. Eng., Innbruck Austria, 2004.

[9]

F. Liu, B. Zhao, P. K. Kijewski, L. Wang, and L. H. Schwartz, "Liver segmentation for CT images using GVF snake," Med. Phys., vol. 32, no. 12, pp. 3699-3706, 2005.

[10]

C. Xu and J. L. Prince, "Gradient vector flow: A new external force for snakes," in Proc. IEEE Proc. Conf. Comp. Vis. Pattern Recognit. (CVPR 1997), pp. 66-71.

[11]

C. Xu and J. L. Prince, "Snakes, shapes, and gradient vector flow," IEEE Trans. Image Process., vol. 7, pp. 359-369, Mar. 1998.

Digital Library

[12]

M. Pham, R. Susomboon, T. Disney,D. Raicu, and J. Furst, "A comparison of texture models for automatic liver segmentation," presented at the SPIE Med. Imag. Conf., San Diego, CA, 2007.

[13]

S. Geman and D. Geman, "Stochastic relaxation, gibbs distributions, and the bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721-741, Nov. 1998.

Digital Library

[14]

S. J. Lim, Y. Y. Jeong, and Y. S. Ho, "Automatic liver segmentation for volume measurement in CT images," JVCIR, vol. 17, no. 4, pp. 860-875, 2006.

[15]

F. Daisuke, S. Akinobu, and K. Hidefumi, "Automatic Liver Segmentation Method based onMaximum A Posterior Probability Estimation and Level Set Method," in Proc. Med. Image Comput. Comput.-Assisted Intervention (MICCAI 2007) Workshop 3D Segmentation Clin.: Grand Challenge, 2007, pp. 117-124.

[16]

T. Okada, R. Shimada,Y. Sato,M.Hori, K.Yokota, M. Nakamoto,Y.Chen, H. Nakamura, and S. Tamura, "Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model," in Proc. Med. Image Comput. Comput.-Assisted Intervention (MICCAI 2007), pp. 86-93.

[17]

T. Heimann, I. Wolf, and H. P. Meinzer, "Active shape models for a fully automated 3d segmentation of the liver--An evaluation on clinical data," in Proc. Med. Image Comput. Comput.-Assisted Intervention (MICCAI 2006), pp. 41-48.

[18]

M. Kass, A.Witkin, and D. Terzopolous, "Snake: Active contour models," in Proc. 1st Int. Conf. Comput. Vis., 1987, pp. 259-268.

[19]

T. Chan and L.Vese, "Active contourswithout edges," IEEE Trans. Image Process., vol. 10, no. 2, pp. 266-277, Feb. 2001.

Digital Library

[20]

H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, and H. C. Wolf, "Parametric correspondence and chamfer matching: Two new techniques for image matching," in Proc 5th Int. Joint Conf. Artif. Intell., 1997, pp. 659- 663.

[21]

G. Borgefors, "Hierarchical chamfer matching: A parametric edge matching algorithm," IEEE Trans. Pattern Anal. Mach. Intell., vol. 10, no. 6, pp. 849-865, Nov. 1988.

Digital Library

[22]

Y. Jin, A. F. Laine, and C. Imielinska, "An adaptive speed term based homogeneity for level set segmentation," in Proc. Med. Imag. SPIE, 2002, vol. 4684, no. 1, pp. 383-390.

[23]

D. Adalsteinsson and J. Sethian, "A fast level set method for propagating interfaces," J. Comput. Phys., vol. 118, pp. 269-277, 1995.

Digital Library

[24]

M. H. Alsuwaiyel, Algorithms Design Techniques and Analysis. Singapore: World Scientific, 1998, pp. 471-474.

[25]

J. O'Rourke, Computational Geometry in C. New York: Cambridge Univ. Press, 1994.

Digital Library

[26]

K. H. Wong, J.W. VanMeter, S. T. Fricke, C. R. Maure, Jr., and K. Clearya, "MRI for modeling of liver and skin respiratory motion," in Proc. CARS 2004, pp. 747-752.

[27]

T. Rohlfing, C. R. Maurer, Jr., W. G. O'Dell, and J. Zhong, "Modeling liver motion and deformation during the respiratory cycle using intensity-based free-from registration of gated MR images," in Proc. SPIE, 2001, vol. 4319, pp. 337-348.

[28]

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, 1998.

Digital Library

[29]

R. Malladi and J. A. Sethian, "An O(N log N) algorithm for shape modeling," Proc. Nat. Acad. Sci., vol. 93, pp. 9389-9392, 1996.

[30]

R. Materne, B. E. Van Beers, A. M. Smith, I. Leconte, J. Jamart, J. P. Dehoux, A. Keyeux, and Y. Horsmans, "Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model," Clin. Sci (London), vol. 99, pp. 517-525, 2000.

[31]

V. Chalana and Y. Kim, "A methodology for evaluation of boundary detection algorithms on medical images," IEEE Trans. Med. Imag., vol. 16, no. 5, pp. 642-652, Oct. 1997.

[32]

C. E. Metz, "ROC methodology in radiologic imaging," Investigative Radiol., vol. 21, pp. 720-733, 1986.

[33]

F. Aaron and C. Bernard, "Evaluation of segmentation algorithms for medical imaging," in Proc. 2005 IEEE Eng. Med. Biol. 27th Annu. Conf., Shanghai, China, Sep., pp. 7186-7189.

Cited By

Dura EDomingo JGöçeri EMartí-Bonmatí L(2018)A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstructionPattern Analysis & Applications10.5555/3288219.328823521:4(1083-1095)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3288219.3288235
Kim YKim SChoe Y(2016)Robust semi-automated quantification of cardiac MR perfusion using level setComputers in Biology and Medicine10.1016/j.compbiomed.2016.02.01471:C(162-173)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.compbiomed.2016.02.014
Bereciartua APicon AGaldran AIriondo P(2016)3D active surfaces for liver segmentation in multisequence MRI imagesComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.04.028132:C(149-160)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2016.04.028
Show More Cited By

An improved level set for liver segmentation and perfusion analysis in MRIs
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics

Recommendations

A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction

Magnetic resonance (MR) tomographic images are routinely used in diagnosis of liver pathologies. Liver segmentation is needed for these types of images. It is therefore an important requirement for later tasks such as comparison among studies of ...
Liver segmentation in MRI

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical ...
Automatic Liver Segmentation Based on Shape Constrained Differeomorphic Demons Atlas Registration
ICECC '12: Proceedings of the 2012 International Conference on Electronics, Communications and Control

Liver segmentation from abdominal Computer Tomography (CT) images plays an important role in liver disease diagnosis as well as liver surgical planning. In this paper, a hybrid approach is proposed for fully automatic liver position search and liver ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Information Technology in Biomedicine

IEEE Transactions on Information Technology in Biomedicine Volume 13, Issue 1

January 2009

137 pages

ISSN:1089-7771

Issue’s Table of Contents

Copyright © 2009.

Publisher

IEEE Press

Publication History

Published: 01 January 2009

Revised: 25 May 2008

Received: 13 August 2007

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Dura EDomingo JGöçeri EMartí-Bonmatí L(2018)A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstructionPattern Analysis & Applications10.5555/3288219.328823521:4(1083-1095)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3288219.3288235
Kim YKim SChoe Y(2016)Robust semi-automated quantification of cardiac MR perfusion using level setComputers in Biology and Medicine10.1016/j.compbiomed.2016.02.01471:C(162-173)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.compbiomed.2016.02.014
Bereciartua APicon AGaldran AIriondo P(2016)3D active surfaces for liver segmentation in multisequence MRI imagesComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.04.028132:C(149-160)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2016.04.028
Göçeri EGürcan MDicle O(2014)Fully automated liver segmentation from SPIR image seriesComputers in Biology and Medicine10.1016/j.compbiomed.2014.08.00953:C(265-278)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1016/j.compbiomed.2014.08.009
López-Mir FNaranjo VAngulo JAlcañiz MLuna L(2014)Liver segmentation in MRIComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2013.12.022114:1(11-28)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1016/j.cmpb.2013.12.022
Li BChui CChang SOng S(2012)A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT imagesExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.02.09539:10(9661-9668)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.02.095
Gloger OToennies KKuehn J(2011)Fully automatic liver volumetry using 3D level set segmentation for differentiated liver tissue types in multiple contrast MR datasetsProceedings of the 17th Scandinavian conference on Image analysis10.5555/2009594.2009651(512-523)Online publication date: 1-May-2011
https://dl.acm.org/doi/10.5555/2009594.2009651

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents