Methods of artificial enlargement of the training set for statistical shape models

IEEE Transactions on Medical Imaging

Volumn 27, Issue 11, 2008, Pages 1643-1654

  Koikkalainen, Juha ^a   Tölli, Tuomas ^c   Lauerma, Kirsi ^d   Antila, Kari ^b   Mattila, Elina ^b   Lilja, Mikko ^c   Lötjönen, Jyrki ^b  

^a VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

^b Centre of Finland   (Finland)

^c AALTO UNIVERSITY   (Finland)

^d UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Active shape model; Cardiac magnetic resonance imaging (MRI); Point distribution model; Statistical shape model; Training set

Indexed keywords

ANNEALING; ATOMS; DEFORMATION; DIGITAL IMAGE STORAGE; IMAGE PROCESSING; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; METAL WORKING SHAPERS; PRINCIPAL COMPONENT ANALYSIS; REAL TIME SYSTEMS; RESONANCE; SIMULATED ANNEALING; STATISTICAL METHODS;

ACTIVE SHAPE MODEL; CARDIAC MAGNETIC RESONANCE IMAGING (MRI); POINT DISTRIBUTION MODEL; STATISTICAL SHAPE MODEL; TRAINING SET;

FINITE ELEMENT METHOD;

ANATOMY; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; FINITE ELEMENT ANALYSIS; HEART; HISTOLOGY; HUMAN; IMAGE PROCESSING; INFORMATION PROCESSING; INFORMATION RETRIEVAL; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENT ANALYSIS; SAMPLE SIZE; STATISTICAL MODEL; STATISTICS;

ANATOMY, CROSS-SECTIONAL; ARTIFICIAL INTELLIGENCE; AUTOMATIC DATA PROCESSING; FINITE ELEMENT ANALYSIS; HEART; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MAGNETIC RESONANCE IMAGING; MODELS, CARDIOVASCULAR; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; RESEARCH DESIGN; SAMPLE SIZE;

EID: 54949097708     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2008.929106     Document Type: Article

References (31)

1. T. Mclnerney and D. Terzopoulos, "Deformable models in medical image analysis: A survey," Med. Image Anal., vol. 1, pp. 91-108, Jun. 1996.
2. C. Xu, D. Pham, and J. Prince, "Image segmentation using deformable models," in Handbook of Medical Imaging, Volume 2. Medical Imaging Processing and Analysis, M. Sonka and J. Fitzpatrick, Eds. Belligngham, WA: SPIE Press, 2000, pp. 129-174.
3. T. Cootes, C. Taylor, D. Cooper, and J. Graham, "Active shape models-Their training and application," Comput. Vis. Image Understand., vol. 61, pp. 38-59, Jan. 1995.
4. T. Cootes and C. Taylor, Statistical models of appearance for computer vision Univ. Manchester, Manchester, U.K., 2004.
5. A. Frangi, D. Rueckert, J. Schnabel, and W. Niessen, "Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling," IEEE Trans. Med. Imag., vol. 21, no. 9, pp. 1151-1166, Sep. 2002.
6. S. Mitchell, J. Bosch, B. Lelieveldt, R. v. d. Geest, J. Reiber, and M. Sonka, "3-D active appearance models: Segmentation of cardiac MR and ultrasound images," IEEE Trans. Med. Imag., vol. 21, no. 9, pp. 1167-1178, Sep. 2002.
7. D. Rueckert, A. Frangi, and J. Schnabel, "Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration," IEEE Trans. Med. Imag., vol. 22, no. 8, pp. 1014-1025, Aug. 2003.
8. J. Lötjönen, S. Kivistö, J. Koikkalainen, and K. Lauerma, "Statistical shape model of atria, ventricles and epicardium from short- and longaxis MR images," Med. Image Anal., vol. 8, pp. 371-386, Sep. 2004.
9. M. Kaus, J. v. Berg, J. Weese, W. Niessen, and V. Pekar, "Automated segmentation of the left ventricle in cardiac MRI," Med. Image Anal., vol. 8, pp. 245-254, Sep. 2004.
10. H. v. Assen, M. Danilouchkine, A. Frangi, S. Ordás, J. Westenberg, J. Reiber, and B. Lelieveldt, "Spasm: A 3-D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data," Med. Image Anal., vol. 10, pp. 286-303, Apr. 2006.
11. R. Davies, C. Twining, T. Cootes, J. Waterton, and C. Taylor, "A minimum description length approach to statistical shape modeling," IEEE Trans. Med. Imag., vol. 21, no. 5, pp. 525-537, May 2002.
12. M. Kaus, V. Pekar, C. Lorenz, R. Trayen, S. Lobregt, and J. Weese, "Automated 3-D FDM construction from segmented images using deformable models," IEEE Trans. Med. Imag., vol. 22, no. 8, pp. 1005-1013, Aug. 2003.
13. S. Ordas, L. Boisrobert, M. Bossa, M. Huguet, M. Laucelli, S. Olmos, and A. Frangi, "Grid-enabled automatic construction of a two-chamber cardiac FDM from a large database of dynamic 3-D shapes," Proc. IEEE Int. Symp. Biomed. Imag., vol. 1, pp. 416-419, Apr. 2004.
14. C. Davatzikos, X. Tao, and D. Shen, "Hierarchical active shape models, using the wavelet transform," IEEE Trans. Med. Imag., vol. 22, no. 3, pp. 414-423, Mar. 2003.
15. A. Kelemen, G. Székely, and G. Gerig, "Elastic model-based segmentation of 3-D neuroradiological data sets," IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 828-839, Oct. 1999.
16. D. Shen, E. Herskovits, and C. Davatzikos, "Adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures," IEEE Trans. Med. Imag., vol. 20, no. 4, pp. 257-270, Apr. 2001.
17. A. Baumberg and D. Hogg, "An adaptive eigenshape model," Proc. BMVC, pp. 87-96, 1995.
18. T. Cootes and C. Taylor, "Combining point distribution models with shape models based on finite element analysis," Image Vis. Comput., vol. 13, pp. 403-409, Jun. 1995.
19. S. Sclaroff and J. Isidore, "Active blobs," in Proc. ICCV, 1998, pp. 1146-1153.
20. Y. Wang and L. Staib, "Boundary finding with prior shape and smoothness models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 7, pp. 738-743, Jul. 2000.
21. M. d. Bruijne, B. v. Ginneken, M. Viergever, and W. Niessen, "Adapting active shape models for 3-D segmentation of tubular structures in medical images," in Proc. IPMI, 2003, pp. 136-147.
22. J. Weese, M. Kaus, C. Lorenz, S. Lobregt, R. Truyen, and V. Pekar, "Shape constrained deformable models for 3-D medical image segmentation," in Proc. IPMI, 2001, pp. 380-387.
23. Z. Zhao, S. Aylward, and E. Teoh, "A novel 3-D partitioned active shape model for segmentation of brain MR images," in Proc. MICCAI, 2005, pp. 221-228.
24. J. Lotjonen, K. Antila, E. Lamminmaki, J. Koikkalainen, M. Lilja, and T. F. Cootes, "Artificial enlargement of a training set for statistical shape models: Application to cardiac images," in Proc. FIMH, 2005, pp. 92-101.
25. T. Tölli, J. Koikkalainen, K. Lauerma, and J. Lötjönen, "Artificially enlarged training set in image segmentation," in Proc. MICCAI, 2006, pp. 75-82.
26. J. Lötjönen and T. Mäkelä, "Elastic matching using a deformation sphere," in Proc. MICCAI, 2001, pp. 541-548.
27. D. Shen and C. Davatzikos, "An adaptive-focus deformable model using statistical and geometric information," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 906-913, Aug. 2000.
28. R. Duda and P. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
29. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical recipes in C: the art of scientific computing, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 1992.
30. L. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, pp. 297-302, Jul. 1945.
31. M. Lorenzo-Valdés, G. Sanchez-Oitiz, A. Elkington, R. Mohiaddin, and D. Rueckert, "Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm," Med. Image Anal., vol. 8, pp. 255-265, Sep. 2004.