Shape prior modeling using sparse representation and online dictionary learning

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

Volumn 7512 LNCS, Issue , 2012, Pages 435-442

  Zhang, Shaoting ^a   Zhan, Yiqiang ^b   Zhou, Yan ^c   Uzunbas, Mustafa ^b   Metaxas, Dimitris N ^a  

^a RUTGERS UNIVERSITY   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

^c Elekta AB   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

EFFICIENCY; IMAGE SEGMENTATION; ITERATIVE METHODS; LEARNING SYSTEMS; MEDICAL COMPUTING; MEDICAL IMAGING; OBJECT RECOGNITION;

CARDIAC SEGMENTATION; IMAGING APPLICATIONS; ON-LINE LEARNING METHODS; ONLINE DICTIONARY LEARNING; PARAMETRIC MODELING; RUN-TIME EFFICIENCY; SPARSE OPTIMIZATIONS; SPARSE REPRESENTATION;

E-LEARNING;

ALGORITHM; ARTICLE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; COMPUTER SYSTEM; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; ONLINE SYSTEM; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; COMPUTER SIMULATION; COMPUTER SYSTEMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, ANATOMIC; ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

MLCS; MLOWN;

EID: 84872932973     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_54     Document Type: Conference Paper

References (12)

1. Aharon, M., Elad, M., Bruckstein, A.: K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)
2. Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489–509 (2006)
3. Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape model - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
4. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. The Annals of Statistics 32(2), 407–499 (2004)
5. Hufnagel, H., Pennec, X., Ehrhardt, J., Handels, H., Ayache, N.: Shape Analysis Using a Point-Based Statistical Shape Model Built on Correspondence Probabilities. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 959–967. Springer, Heidelberg (2007)
6. Langs, G., Paragios, N., Essafi, S.: Hierarchical 3D diffusion wavelet shape priors. In: ICCV, pp. 1717–1724 (2010)
7. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: ICML, pp. 689–696 (2009)
8. Nesterov, Y., Nemirovsky, A.: Interior point polynomial methods in convex programming. Studies in Applied Mathematics 13, 1993 (1994)
9. Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., Shen, D.: Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Transactions on Medical Imaging 27(4), 481–494 (2008)
10. Yan, P., Kruecker, J.: Incremental Shape Statistics Learning for Prostate Tracking in TRUS. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 42–49. Springer, Heidelberg (2010)
11. Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D., Zhou, X.: Towards robust and effective shape modeling: Sparse shape composition. Medical Image Analysis 16(1), 265–277 (2012)
12. Zhou, Y., Yeniaras, E., Tsiamyrtzis, P., Tsekos, N., Pavlidis, I.: Collaborative Tracking for MRI-Guided Robotic Intervention on the Beating Heart. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 351–358. Springer, Heidelberg (2010)