Robust medical images segmentation using learned shape and appearance models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5761 LNCS, Issue PART 1, 2009, Pages 281-288

  El Baz, Ayman ^a   Gimel'farb, Georgy ^b  

^a University of Louisville   (United States)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

COMMON CENTROID; DEFORMABLE MODELS; DISCRETE GAUSSIANS; EXPECTATION MAXIMIZATION; GIBBS POTENTIAL; GRAY LEVELS; INTERACTION GEOMETRIES; LINEAR COMBINATIONS; MARKOV-GIBBS RANDOM FIELDS; MEDICAL IMAGES; PAIRWISE INTERACTION; PARAMETRIC MODELS; SHAPE AND APPEARANCE MODEL; SHAPE PRIORS; TRAINING DATA; TRAINING SUBSETS; VISUAL APPEARANCE;

IMAGE SEGMENTATION; VISUALIZATION;

MEDICAL COMPUTING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; KIDNEY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; KIDNEY; MAGNETIC RESONANCE IMAGING; MODELS, ANATOMIC; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 79954565310     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04268-3_35     Document Type: Conference Paper

References (16)

1. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape based approach to the segmentation of medical imagery using level sets. IEEE TMI 22, 137-154 (2003)
2. Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78-92. Springer, Heidelberg (2002)
3. Pentland, A., Sclaroff, S.: Closed-form solutions for physically based shape modeling and recognition. IEEE PAMI 13, 715-729 (1991) (Pubitemid 21674671)
4. Staib, L., Duncan, J.: Boundary finding with parametrically deformable contour models. IEEE PAMI 14, 1061-1075 (1992) (Pubitemid 23594855)
5. Pizer, S., Gerig, G., Joshi, S., Aylward, S.: Multiscale medial shape-based analysis of image objects. Proc. IEEE 91, 1670-1679 (2003) (Pubitemid 40890810)
6. Styner, M., Gerig, G., Joshi, S., Pizer, S.: Automatic and robust computation of 3D medial models incorporating object variability. Int. J. Computer Vision 55, 107-122 (2002)
7. Huang, X., Metaxas, D., Chen, T.: MetaMorphs: Deformable shape and texture models. In: Proc. CVPR, vol. 1, pp. 496-503 (2004)
8. Leventon, M., Grimson, E., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. CVPR, pp. 316-323 (2000)
9. Shen, D., Davatzikos, C.: An adaptive-focus deformable model using statistical and geometric information. IEEE PAMI 22, 906-913 (2000)
10. Chen, Y., Thiruvenkadam, S., Tagare, H., Huang, F.,Wilson, D., Geiser, E.: On the incorporation of shape priors into geometric active contours. In: IEEE Workshop Variational and Level Set Methods, pp. 145-152 (2001)
11. Pohl, K.M., Kikinis, R., Wells, W.M.: Active mean fields: Solving the mean field approximation in the level set framework. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 26-37. Springer, Heidelberg (2007)
12. Song, Z., Awate, S.P., Licht, D.J., Gee, J.C.: Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based markov priors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 883-890. Springer, Heidelberg (2007)
13. Awate, S., Tasdizen, T., Foster, N., Whitaker, R.: Adaptive markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification. Medical Image Analysis 10(5), 726-739 (2006) (Pubitemid 44283805)
14. Lowe, D.G.: Distinctive image features from scale-invariant Keypoints. Int. J. Computer Vision 60, 91-110 (2004)
15. El-Baz, A., Gimel'farb, G.: EM Based Approximation of Empirical Distributions with Linear Combinations of Discrete Gaussians. In: ICIP 2007, San Antonio, Texas, USA, September 16-19, 2007, vol. IV, pp. 373-376 (2007)
16. Cootes, T., Taylor, C.: A mixture model for representing shape variation. In: Proc. of British Machine Vision, pp. 110-119 (1997)