Shape-based diagnosis of the aortic valve

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7259, Issue , 2009, Pages

  Ionasec, Razvan Ioan ^a,c   Tsymbal, Alexey ^b   Vitanovski, Dime ^a   Georgescu, Bogdan ^a   Zhou, S Kevin ^a   Navab, Nassir ^c   Comaniciu, Dorin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b SIEMENS AG   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Case retrieval; Computer aided diagnosis; Discriminative distance function; Machine learning; Physiological aortic valve modeling

Indexed keywords

2D IMAGES; 3D GEOMETRICAL MODEL; AORTIC ROOT; AORTIC VALVES; CARDIAC CT; CARDIO-VASCULAR DISEASE; CASE RETRIEVAL; CLASS-BASED; DISTANCE FUNCTIONS; END-DIASTOLIC; EQUIVALENCE CONSTRAINTS; MACHINE-LEARNING; MODEL PARAMETERS; NOVEL DIAGNOSTICS; PARAMETRIC MODELS; ROBUST SEGMENTATION; SHAPE BASED; SHAPE INFORMATION; VOLUMETRIC DATA;

BLOOD VESSELS; EQUIVALENCE CLASSES; FUNCTIONS; IMAGE PROCESSING; IMAGING SYSTEMS; MEDICAL IMAGING; ROBOT LEARNING; THREE DIMENSIONAL; VALVES (MECHANICAL); VOLUMETRIC ANALYSIS;

COMPUTER AIDED DIAGNOSIS;

EID: 71749096255     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.812488     Document Type: Conference Paper

References (19)

1. Nkomo, V., Gardin, J., Skelton, T., Gottdiener, J., Scott, C., and Enriquez-Sarano, M., "Burden of valvular heart diseases: a population-based study.," Lancet 368(9540), 1005-1011 (2006).
2. Dagum, P., Green, G., Nistal, F., Daughters, G., Timek, T., Foppiano, L., Bolger, A., Ingels, N., and Miller, D., "Deformational dynamics of the aortic root: modes and physiologic determinants.," Circulation 100(19 Suppl), II54-II62 (1999).
3. Anderson, R., "The surgical anatomy of the aortic root," Multimedia Manual of Cardiothoracic Surgery (MMCTS) (2006). doi:10.1510/mmcts.2006.002527.
4. Vahanian, A., Baumgartner, H., Bax, J., Butchart, E., Dion, R., Filippatos, G., Flachskampf, F., Hall, R., Iung, B., Kasprzak, J., Nataf, P., Tornos, P., Torracca, L., and Wenink, A., "Guidelines on the management of valvular heart disease: The task force on the management of valvular heart disease of the european society of cardiology)," European heart journal 28(2), 230-268 (2007).
5. Ionasec, R. I., Georgescu, B., Comaniciu, D., Vogt, S., Schoepf, U., and Gassner, E., "Patient specific 4d aortic root models derived from volumetric image data sets," in [Radiological Society of North American (RSNA)], (November 2008).
6. Gassner, E., Ionasec, R. I., Georgescu, B., Vogt, S., Schoepf, U., and Comaniciu, D., "Performance of a dynamic aortic valve model for quantification of the opening area at cardiac mdct . comparison to manual planimetry," in [Radiological Society of North American (RSNA)], (November 2008).
7. Pouleur, A., le Polain de Waroux, J., Pasquet, A., Vanoverschelde, J., and Gerber, B., "Aortic valve area assessment: multidetector ct compared with cine mr imaging and transthoracic and transesophageal echocardiography, " Radiology 244(3), 745-754 (2007).
8. Ionasec, R. I., Georgescu, B., Gassner, E., Vogt, S., Kutter, O., Scheuering, M., Navab, N., and Comaniciu, D., "Dynamic model-driven quantification and visual evaluation of the aortic valve from 4d ct," in [International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)], (September 2008).
9. Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., and Comaniciu, D., "Fast automatic heart chamber segmentation from 3d ct data using marginal space learning and steerable features," Proc. International Conference on Computer Vision (2007).
10. Bar-Hillel, A. and Weinshall, D., "Learning distance functions by coding similarity," in [24th Int. Conf. on Machine Learning], 227, 1031-1038 (2007).
11. Tu, Z., "Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering," in [ICCV 2005], 2, 1589-1596 (2005).
12. Duchon, J., "Interpolation des fonctions de deux variables suivant le principe de la flexion des plaques minces," RAIRO Analyse Numerique 10, 5-12 (1976).
13. Mahamud, S. and Hebert, M., "The optimal distance measure for object detection," in [Proc. of Int. Conf. on Computer Vision and Pattern Recognition], 1, 248-255 (2003).
14. Zhou, S., Shao, J., Georgescu, B., and Comaniciu, D., "Boostmotion: Boosting a discriminative similarity function for motion estimation," in [Proc. CVPR], (2006).
15. Hertz, T., Learning distance functions: algorithms and applications, PhD thesis, The Hebrew University of Jerusalem (2006).
16. Yu, J., Amores, J., Sebe, N., and Tian, Q., "Toward robust distance metric analysis for similarity estimation," in [Int. Conf. on Computer Vision and Pattern Recognition], 1(316-322) (2006).
17. Freund, Y. and Schapire, R. E., "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences 55(1), 119-139 (1997).
18. Breiman, L., "Random forests," in [Machine Learning], 5-32 (2001).
19. Tsymbal, A., Huber, M., Zillner, S., Hauer, T., and Zhou, K., "Visualizing patient similarity in clinical decision support," in [Alexander Hinneburg], 304 311 (2007).