A novel explicit 2D+t cyclic shape model applied to echocardiography

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

Volumn 5241 LNCS, Issue PART 1, 2008, Pages 527-534

  Casero, Ramón ^a   Noble, J Alison ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIAC CYCLES; COMPUTING MODEL; CONTOUR POINTS; DATA SETS; DYNAMIC EFFECTS; IMPLICIT MODEL; KERNEL PCA; MULTI-DIMENSIONAL SCALING; MYOCARDIAL CONTOURS; POINT DISTRIBUTION MODELS; PROCRUSTES; SHAPE MODEL;

COMPUTER SCIENCE; ECHOCARDIOGRAPHY; MEDICAL COMPUTING;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ECHOCARDIOGRAPHY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; ECHOCARDIOGRAPHY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 58849157007     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85988-8_63     Document Type: Conference Paper

References (12)

1. Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24(6), 417-441 (1933)
2. Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, 4(3), 519-524 (1987)
3. Cootes, T., Taylor, C., Cooper, D., Graham, J.: Training models of shape from sets of examples. In: Procs. of the BMVC, pp. 266-275. Springer, Berlin (1992)
4. Cootes, T., Taylor, C.: Active shape models - 'Smart snakes'. In: Procs. of the BMVC, pp. 266-275. Springer, Heidelberg (1992)
5. Edwards, G., Taylor, C., Cootes, T.: Learning to identify and track faces in image sequences. In: Procs. of the BMVC (1997)
6. Mitchell, S., Bosch, J., Lelieveldt, B., van der Geest, R., Reiber, J., Sonka, M.: 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Transactions on Medical Imaging 21(9), 1167-1178 (2002)
7. Bosch, J., Mitchell, S., Lelieveldt, B., Nijland, F., Kamp, O., Sonka, M.: Automatic segmentation of echocardiography sequences by active appearance motion models. IEEE Transactions on Medical Imaging 21(11), 1374-1383 (2002)
8. Stegmann, M., Pedersen, D.: Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation. In: Proc. of SPIE, International Symposium on Medical Imaging 2005, vol. 5747, pp. 336-350 (2005)
9. Henry, R., Lewis, C., Hopke, P., Williamson, H.: Review of receptor model fundamentals. Atmospheric Environment 18, 1507-1515 (1984)
10. Gower, J.: Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3,4), 325-338 (1966)
11. Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299-1319 (1998)
12. Rohlf, F., Slice, D.: Extensions of the Procrustes method for the optimal superimposition of landmarks. Systematic Zoology 39(1), 40-59 (1990)