Multi-compartment heart segmentation in CT angiography using a spatially varying gaussian classifier

International Journal of Computer Assisted Radiology and Surgery

Volumn 7, Issue 6, 2012, Pages 829-836

  Murphy, S ^a   Akinyemi, A ^a   Steel, J ^a   Petillot, Y ^b   Poole, I ^a  

^a Toshiba Medical Visualization Systems Europe Ltd   (United Kingdom)

^b HERIOT WATT UNIVERSITY   (United Kingdom)

Author keywords

Atlas based segmentation; Classification; Computed tomography angiography; Image analysis; Probabilistic modelling

Indexed keywords

ANGIOCARDIOGRAPHY; ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; DIAGNOSTIC ACCURACY; GAUSSIAN CLASSIFIER; IMAGE DISPLAY; IMAGE PROCESSING; KERNEL METHOD; PRIORITY JOURNAL; RADIATION FIELD; SIGNAL DETECTION; STATISTICAL PARAMETERS; SURFACE PROPERTY;

EID: 84872190148     PISSN: 18616410     EISSN: 18616429     Source Type: Journal    
DOI: 10.1007/s11548-012-0695-4     Document Type: Article

References (20)

1. Fritz D, Daniel R, Unterhinninghofen R, Dillmann R, Scheuering M (2005) Automatic segmentation of the left ventricle and computation of diagnostic parameters using region growing and a statistical model. In Proc. SPIE, 5747:1844-1854. Citeseer,
2. Cootes TF, Hill A, Taylor CJ, Haslam J (1994) The use of active shape models for locating structures in medical images. Distribution 12(6):355-366
3. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681-685
4. Stegmann MB (2005) Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation. In: Proceedings of SPIE, vol 5747, pp 336-350. SPIE, 2005
5. Mitchell SC, Lelieveldt BP, van der Geest RJ, Bosch HG, Reiber JH, Sonka M (2001) Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 20(5):415-423
6. McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: A survey. Med Image Anal 1(2):91-108
7. McInerney T, Terzopoulos D (1994) A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. Comput Med Imaging Graph Off J Comput Med Imaging Soc 19(1):69-83
8. Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, Vembar M, Olszewski ME, Subramanyan K, Lavi G, Weese J (2008) Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging 27(9):1189-1201
9. Ecabert O, Peters J, Walker MJ, Ivanc T, Lorenz C, von Berg J, Lessick J, Vembar M, Weese J (2011) Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med Image Anal 15(6):863-876
10. Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11):1668-1681
11. Lorenzo-Valdés M, Sanchez-Ortiz G, Mohiaddin R, Rueckert D (2002) Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In: Dohi T, Kikinis R (eds) Medical image computing and computer-assisted intervention-MICCAI 2002. Lecture notes in computer science, vol 2488. Springer-Verlag, Berlin Heidelberg, pp 642-650
12. Lorenzo-Valdez M, Sanchez-Ortiz G, Elkington AG, Mohiaddin RH, Rueckert D (2004) Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal 8:255-265
13. Kirisli HA, Schaap M, Klein S, Papadopoulou SL, Bonardi M, Chen CH, Weustink AC, Mollet NR, Vonken EJ, van der Geest RJ, vanWalsum T, NiessenWJ (2010) Evaluation of amulti-atlasbased method for segmentation of cardiac CTA data: A large-scale, multicenter, and multivendor study. Med Phys 37(12):6279
14. Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903-921
15. Depa M, Holmvang G, Schmidt EJ, Golland P, SabuncuMR (2011) Towards efficient label fusion by pre-alignment of training data. MICCAI 2011 workshop on multi-atlas labeling and statistical fusion, pp 1-9
16. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van Der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341-355
17. Knutsson H, Westin CF (1993) Normalised and differential convolution: methods for interpolation and filtering of incomplete and uncertain data. In: Proceedings of computer vision and pattern recognition, New York City, USA, IEEE
18. Gelman A (2006) Prior distributions for variance parameters in hierarchical models. Bayesian Anal 34(3):377-390
19. Powell MJD (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7(2):155
20. Crum WR, Griffin LD, Hawkes DJ (2003) Zen and the art of medical image registration: correspondence, homology, and quality. In Pract 20:1425-1437