Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography

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

Volumn 5528, Issue , 2009, Pages 447-456

  Lempitsky, Victor ^a   Verhoek, Michael ^b   Noble, J Alison ^b   Blake, Andrew ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC SOLUTIONS; CLASSIFICATION TASKS; DISCRIMINATIVE CLASSIFIERS; ISCHAEMIA; LARGE SIZES; LOW SIGNAL-TO-NOISE RATIO; MACHINE LEARNING COMMUNITIES; MACHINE-LEARNING; MYOCARDIAL TISSUE; RANDOM FOREST CLASSIFICATION; RANDOM FORESTS; REAL-TIME 3D ECHOCARDIOGRAPHY; WALL MOTION; WALL THICKENING;

ECHOCARDIOGRAPHY; ROBOT LEARNING; SIGNAL TO NOISE RATIO;

THREE DIMENSIONAL;

EID: 68849096223     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01932-6_48     Document Type: Conference Paper

References (26)

1. Lynch, M., Ghita, O., Whelan, P.: Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge. Computerized Medical Imaging and Graphics 30(4), 255-262 (2006)
2. Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9(7), 1545-1588 (1997)
3. Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001)
4. Winn, J.M., Shotton, J.: The layout consistent random field for recognizing and segmenting partially occluded objects. In: CVPR (1), pp. 37-44 (2006)
5. Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR (2008)
6. Schroff, F., Criminisi, A., Zisserman, A.: Object class segmentation using random forests. In: BMVC (2008)
7. Sharp, T.: Implementing decision trees and forests on a GPU. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 595-608. Springer, Heidelberg (2008)
8. Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: ICML, pp. 161-168 (2006)
9. Noble, J., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987-1010 (2006)
10. Yan, J., Zhuang, T.: Applying improved fast marching method to endocardial boundary detection in echocardiographic images. Pattern Recognition Letters 24(15), 2777-2784 (2003)
11. Angelini, E.D., Homma, S., Pearson, G., Holmes, J.W., Laine, A.F.: Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: A clinical study on right and left ventricles. Ultrasound in Medicine & Biology 31(9), 1143-1158 (2005)
12. Jacob, G., Noble, J., Behrenbruch, C., Kelion, A., Banning, A.: A shape-space-based approach to tracking myocardial borders and quantifying regional left- ventricular function applied in echocardiography. IEEE Trans. Med. Imaging 21(3), 226-238 (2002)
13. Zhu, Y., Papademetris, X., Sinusas, A., Duncan, J.S.: Segmentation of myocar-dial volumes from real-time 3D echocardiography using an incompressibility constraint. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 44-51. Springer, Heidelberg (2007)
14. Nillesen, M.M., Lopata, R.G., Gerrits, I.H., Kapusta, L., Thijssen, J.M., de Korte, C.L.: Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images. Ultrasound in Medicine & Biology 34(4), 674-680 (2008)
15. Tao, Z., Tagare, H., Beaty, J.: Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE Trans. Med. Imaging 25(11), 1483-1491 (2006)
16. Chang, R., Wu, W., Moon, W.K., Chou, Y., Chen, D.: Support vector machines for diagnosis of breast tumors on US images. Academic Radiology 10(2), 189-197 (2003)
17. Drukker, K., Giger, M.L., Vyborny, C.J., Mendelson, E.B.: Computerized detection and classification of cancer on breast ultrasound1. Academic Radiology 11(5), 526-535 (2004)
18. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML, pp. 148-156 (1996)
19. Pujol, O., Rosales, M., Radeva, P., Nofrerias-Fernández, E.: Intravascular ultra-sound images vessel characterization using adaboost. In: Magnin, I.E., Montagnat, J., Clarysse, P., Nenonen, J., Katila, T. (eds.) FIMH 2003. LNCS, vol. 2674, pp. 242-251. Springer, Heidelberg (2003)
20. Georgescu, B., Zhou, X.S., Comaniciu, D., Gupta, A.: Database-guided segmentation of anatomical structures with complex appearance. In: CVPR (2) (2005)
21. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81-106 (1986)
22. Andres, B., Köthe, U., Helmstaedter, M., Denk, W., Hamprecht, F.A.: Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 142-152. Springer, Heidelberg (2008)
23. Hope, T.A., Gregson, P.H., Linney, N.C., Schmidt, M.H., Abdolell, M.: Selecting and assessing quantitative early ultrasound texture measures for their association with cerebral palsy. IEEE Trans. Med. Imaging 27(2), 228-236 (2008)
24. Vibha, L., Harshavardhan, G.M., Pranaw, K., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M.: Lesion detection using segmentation and classification of mammograms. In: AIAP 2007, pp. 311-316 (2007)
25. Viola, P.A., Jones, M.J.: Robust real-time face detection. In: ICCV, p. 747 (2001)
26. van Rijsbergen, C.: Information Retrieval. Butterworth (1979)