A Review of Snake Models in Medical MR Image Segmentation
Keywords:Deformable models, active contour, snake, magnetic resonance imaging, image segmentation
AbstractDeveloping an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal of the current status of semi-automated and automated methods for the segmentation of MR images with important issues and terminologies. Advantages and disadvantages of various segmentation methods with salient features and their relevancies are also cited.
http://www.radiologyassistant.nl/. Accessed on: January, 12, 2012.
Louis D. N., Ohgaki H., Wiestler O. D., Cavenee, W. K. (Eds.). 2007. Classification of Tumors of the Central Nervous System, (IARC), Lyon.
Alpert, S., Galun, M., Basri, R., and Brandt, A. June 2007. Image Segmentation by Probabilistic Bottom-up Aggregation and Cue Integration. In Computer Vision and Pattern Recognition, IEEE Conference. 1â€“8.
M. Kass, A. Witkin, and D. Terzopoulos. 1987. Snakes-active Contour Models. Int. J. Computer. Vis. 1: 321â€“33.
D. Terzopoulos, A. Witkin, and M. Kass. 1988. Constraints on Deformable Models-recovering 3D Shape and Nonrigid Motion. Art. Intel. 36: 91â€“123.
M. Gastaud, M. Barlaud, and G. Aubert. May 2004. Combining Shape Prior and Statistical Features for Active Contour Segmentation. IEEE Trans. Circuits Syst. Video Technol. 14(5): 726â€“734.
J.-O. Lachaud and A. Montanvert. 1999. Deformable Meshes with Automated Topology Changes for Coarse-to-Fine Three-dimensional Surface Extraction. Med. Image Anal. 3: 187â€“20.
T. McInemey and D. Terzopoulos. Oct. 1999. Topology Adaptive Deformable Surfaces for Medical Image Volume Segmentation. IEEE Trans. Med. Image. 18(10): 840â€“850.
T. McInerney and D. Terzopoulos. 1995. A Dynamic Finite Element Surface Model for Segmentation and Tracking in Multidimensional Medical Images with Application to Cardiac 4D Image Analysis. Computer Med. Image Graph. 19: 69â€“83.
N. Ray and S. T. Acton. Dec. 2004. Motion Gradient Vector Flow: An External Force for Tracking Rolling Leukocytes with Shape and Size Constrained Active Contours. IEEE Trans. Med. Image. 23(12): 1466â€“1478.
N. Ray, S. T. Acton, and K. Ley. Oct. 2002. Tracking Leukocytes in Vivo with Shape and Size Constrained Active Contours. IEEE Trans. Med. Image. 21(10): 1222â€“1235.
A. R. Mansouri, D. P. Mukherjee, and S. T. Acton. Jun. 2004. Constraining Active Contour Evolution via Lie Groups of Transformation. IEEE Trans. Image Process. 13(6): 853â€“863.
N. Paragios and R. Deriche. Mar. 2000. Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. IEEE Trans. Pattern Anal. Mach. Intell. 22(3): 266â€“280.
T. F. Cootes, G. J. Edwards, and C. J. Taylor. Jun. 2001. Active Appearance Models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6): 681â€“685.
T. F. Cootes, C. J. Talyor, D. H. Cooper, and J. Graham. 1995. Active Shape Models-Their Training and Applications. Computer Vis. Image Understand. 61: 38â€“59.
J. A. Sethian. 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. U.K.: Cambridge Univ.
D. Terzopoulos and T. McInerey. 1996. Deformable Models in Medical Image Analysis: A Survey. Med. Image Anal. 1: 91â€“108.
Damadian, R., Goldsmith, M. & Minkoff, L. 1977. NMR in Cancer: XVI. FONAR Image of the Live Human Body. Physiological Chemistry and Physics. 9(1): 97â€“100, ISSN: 0031-9325.
Novelline, R. A. & Squire, L. F. 2004. Squire's Fundamentals of Radiology. Harvard Univ. Press. ISBN 0674012798
A. O Rodriguez. 2004. Principles of Magnetic Resonance Imaging. 50(3): 272â€“286.
www.teamrads.com/Cases/Neuroanatomy/MRI. Accessed on: January, 2012.
C. Westbrook. 2002. MRI at Glance. Blackwell Science Publishing.
Prima, S., Ayache, N., Barrick, T. & Roberts, N. 2001. Maximum Likelihood Estimation of the Bias Field in MR Brain Images: Investigating Different Modeling of the Imaging Process. Processing of Medical Image Computing and Computer-Assisted Intervention. 2208: 811â€“819.
Li, X., Li, L., Lu, H., Chen, D. & Liang, Z. 2003. Inhomogeneity Correction for Magnetic Resonance Images with Fuzzy C-Mean Algorithm. Proc. of SPIE. 5032.
Ruan, S., Jaggi, C., Xue, J., Fadili, J. & Bloyet, D. 2000. Brain Tissue Classification of Magnetic Resonance Images Using Partial Volume Modeling. IEEE Trans. Medical Imaging. 19(12): 1179â€“1187.
W. Choi, K. Lam and W. Siu. 2001. An Adaptive Active Contour Model for Highly Irregular Boundaries. Pattern Recognition. 34: 323â€“331.
A. Singh, L. von Kurowski, and M. Chiu. 1993. Cardiac MR Image Segmentation Using Deformable Models. In Biomedical Image Processing and Biomedical Visualization. 1905: 8â€“28.
L. D. Cohen and I. Cohen. 1993. Finite-element Methods for Active Contour Models and Balloons for 2-D and 3-D Images. IEEE Trans. on Pattern Analysis and Machine Intelligence. 15: 1131â€“1147.
C. Xu and J. L. Prince. 1998. Snakes, Shapes and Gradient Vector Flow. IEEE Trans. on Image Processing. 7(3): 359â€“369.
R. Ronfard. 1994. Region-based Strategies for Active Contour Models. Intâ€™l Journal of Computer Vision. 13(2): 229251.
S. Zhu and A. Yuille. 1996. Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence. 18(9): 884â€“900,
R. Huang, V. Pavlovic, and D. Metaxas. 2004. A Graphical Model Framework for Coupling MRFs and Deformable Models. IEEE Conf. on Computer Vision and Pattern Recognition.
C. Florin, J. Williams, and N. Paragios. 2006. Globally Optimal Active Contours, Sequential Monte Carlo and On-line Learning for Vessel Segmentation. European Conf. on Computer Vision.
Malladi, R., Sethian, J. A., Vemuri, B. 1993. A Topology Independent Shape Modeling Scheme. SPIE Conf. on Geometric Methods in Computer Vision II. 2031: 246â€“58.
R. Malladi, J. Sethian, and B. Vemuri. 1995. Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans. on Pattern Analysis and Machine Intelligence. 17(2): 158â€“175.
Caselles, V., Kimmel, R., Sapiro, G. 1997. Geodesic Active Contours. Int. J. of Comp. Vision. 22: 61â€“79.
T. Jones and D. Metaxas. 1997. Automated 3D Segmentation using Deformable Models and Fuzzy Affinity. Information Processing in Medical Imaging.
Chan, T., Vese, L. A., 1999. An Active Contour Without Edge. Int. Conf. Scale-Space Theories in Computer Vision. 141â€“151.
Paragios, N. 2002. A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis. International Journal of Computer Vision. 50: 345â€“62.
N. Rougon and E Preteux. 1993. Directional Adaptive Deformable Models for Segmentation with Application to 2D and 3D Medical Images. In Medical Imaging 93; Image Processing, volume 1898. 193â€“207. Bellingham, WA.
Carlbom, D. Terzopoulos, and K. Harris. 1994. Computer Assisted Registration, Segmentation, and 3D Reconstruction from Images of Neuronal Tissue Sections. IEEE Trans. On Medical Imaging. 13(2): 351â€“362.
A. Gupta, T. Oâ€™Donnell, and A. Singh. Sep. 1994. Segmentation and Tracking of Cine Cardiac MR and CT Images using a 3-D Deformable Model. In Proc. IEEE Con. on Computers in Cardiolog.
S. Lobregt and M. Viergever. March 1995. A Discrete Dynamic Contour Model. IEEE Trans. on Medical Imaging. 14(1): 12â€“24.
G. Tsechpenakis, B. Lujan, O. Martinez, G. Gregori, and P.J. Rosenfeld. Sept. 2008. Geometric Deformable Model Driven by CoCRFs: Application to Optical Coherence Tomography. Intâ€™l Conf. on Medical Image Computing and Computer Assisted Intervention, NYC, NY.
G. Tsechpenakis, and D. Metaxas. 2007. CRF-driven Implicit Deformable Model. IEEE Conf. on Computer Vision and Pattern Recognition.
L. Chang, H. Chen, and J. Ho. July 1991. Reconstruction of 3D Medical Images: A Nonlinear Interpolation Technique for Reconstruction of 3D Medical Images. Computer Vision, Graphics, and Image Proc. 382â€“391.
G. Tsechpenakis, J. Wang, B. Mayer, and D. Metaxas. 2007. Coupling CRFs and Deformable Models for 3D Medical Image Segmentation. IEEE Mathematical Methods in Biomedical Image Analysis.
C. S. Poon, M. Braun, R. Fahrig, A. Ginige, and A. Dorrell. Segmentation of Medical Images Using an Active Contour Model Incorporating Region-based Images Features. In Robb.
N. Rougon and E Preteux. 1991. Deformable Markers: Mathematical Morphology for Active Contour Models Control. In Image Algebra and Morphological Image Processing 11. 1568: 78â€“89. Bellingham.
I. Herlin, C. Nguyen, and C. Graffigne. June 1992. A Deformable Region Model Using Stochastic Processes Applied to Echocardiographic Images. In. CVPR 921. 534â€“539. Los Alamitos, IEEE Computer Society Press.
J. Gauch, H. Pien, and J. Shah. Hybrid. 1994. Boundary-based and Region-based Deformable Models for Biomedical Image Segmentation. In Mathematical Methods in Medical Imaging. 2299: 72â€“83.
G. Sapiro, R. Kimmel, and V. Caselles. 1995. Object Detection and Measurements in Medical Images Via Geodesic Deformable Contours. In Vision Geometry IV. 2573: 366â€“37.
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