Real-time 3D image segmentation by user-constrained template deformation

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

Volumn 7510 LNCS, Issue , 2012, Pages 561-568

  Mory, Benoît ^a   Somphone, Oudom ^a   Prevost, Raphael ^a   Ardon, Roberto ^a  

^a PHILIPS RESEARCH LABORATORIES   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED OPTIMIZATION; DEFORMATION; IMAGE ENHANCEMENT; LAGRANGE MULTIPLIERS; MEDICAL COMPUTING; MEDICAL IMAGING; ULTRASONIC APPLICATIONS; VISUAL COMMUNICATION;

3D IMAGE SEGMENTATION; AUGMENTED LAGRANGIAN APPROACH; CONTRAST ENHANCED ULTRASOUND; FAST IMPLEMENTATION; INEQUALITY CONSTRAINT; INTERACTIVE IMAGE SEGMENTATION; REAL TIME VISUAL FEEDBACK; SEGMENTATION RESULTS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; DIAGNOSTIC IMAGING; ECHOGRAPHY; HUMAN; IMAGE PROCESSING; IMAGE SUBTRACTION; KIDNEY; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; STATISTICAL MODEL; THEORETICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; KIDNEY; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SOFTWARE; SUBTRACTION TECHNIQUE; ULTRASONOGRAPHY;

MLCS; MLOWN;

EID: 84872534290     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33415-3_69     Document Type: Conference Paper

References (16)

1. Cootes, T.F., et al.: Active shape models: Their training and application. CVIU 61(1), 38–59 (1995)
2. Paragios, N., Rousson, M., Ramesh, V.: Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.)ECCV 2002, Part II. LNCS, vol. 2351, pp. 775–789. Springer, Heidelberg (2002)
3. Cremers, D., et al.: Towards recognition-based variational segmentation using shape priors and dynamic labeling. In: Scale Space, pp. 388–400 (2003)
4. Chan, T.F., Zhu, W.: Level set based shape prior segmentation. In: IEEE CVPR, pp. II:1164–II:1170 (2005)
5. Foulonneau, A., et al.: Multi-reference shape priors for active contours. IJCV 81(1), 68–81 (2009)
6. Leventon, M.E., et al.: Statistical shape influence in geodesic active contours. In: IEEE CVPR, pp. 316–323 (2000)
7. Tsai, A., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE TMI 22(2), 137–154 (2003)
8. Bresson, X., et al.: A variational model for object segmentation using boundary information and shape prior driven by the Mumford-Shah functional. IJCV 68(2), 145–162 (2006)
9. Cremers, D., et al.: Shape statistics in kernel space for variational image segmentation. Pattern Recognition 36(9), 1929–1943 (2003)
10. Saddi, K.A., et al.: Global-to-local shape matching for liver segmentation in ct imaging. In: MICCAI(October 2007)
11. Somphone, O., Mory, B., Makram-Ebeid, S., Cohen, L.: Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity. In: Forsyth, D., Torr, P., Zisserman, A. (eds.)ECCV 2008, Part III. LNCS, vol. 5304, pp. 628–641. Springer, Heidelberg (2008)
12. Huang, X., Metaxas, D.: Metamorphs: Deformable shape and appearance models. IEEE Trans. PAMI 30(8), 1444–1459 (2008)
13. Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: CVPR, vol. 1, pp. 755–762 (June2005)
14. Zhu, S.C., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. PAMI 18(9), 884–900 (1996)
15. Yezzi, A., Soatto, S.: Deformotion: Deforming motion, shape average and the joint registration and approximation of structures in images. IJCV 53(2), 153–167 (2003)
16. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer (August1999)