Automatic recognition of cortical Sulci using a congregation of neural networks

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

Volumn 1935, Issue , 2000, Pages 40-49

  Rivière, Denis ^a   Mangin, Jean François ^a   Papadopoulos Orfanos, Dimitri ^a   Martinez, Jean Marc ^b   Frouin, Vincent ^a   Régis, Jean ^c  

^a DIF   (France)

^b CEA SACLAY   (France)

^c TIMONE HOSPITAL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING;

AUTOMATIC RECOGNITION; CORTICAL FOLDING; GLOBAL FUNCTIONS; LEARNING DATABASE; MULTI LAYER PERCEPTRON; PATTERN RECOGNITION METHOD; RIGHT HEMISPHERES; STRUCTURAL REPRESENTATION;

PATTERN RECOGNITION;

EID: 84945560479     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-40899-4_5     Document Type: Conference Paper

References (25)

1. Ruzena Bajcsy and Chaim Broit. Matching of deformed images. In IEEE Pric. Sixth Int. Conf. on Pattern Recognition, pages 351–353, October 1982.
2. A. Caunce and C. J. Taylor. Using local geometry to build 3D sulcal models. In IPMI’99, LNCS 1613, pages 196–209. Springer Verlag, 1999.
3. H. Chui, J. Rambo, J. Duncan, R. Schultz, and A. Rangarajan. Registration of cortical anatomical structures via robust 3D point matching. In IPMI’99, LNCS 1613, pages 168–181. Springer Verlag, 1999.
4. D. L. Collins, Le Goualher G., and A. C. Evans. Non-linear cerebral registration with sulcal constraints. In MICCAI’98, LNCS-1496, pages 974–984, 1998.
5. J. Declerck, G. Subsol, J.-P. Thirion, and N. Ayache. Automatic retrieval of anatomical structures in 3D medical images. In CVRMed, LNCS-905, pages 153–162, 1995.
6. S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE PAMI, 6(6):721–741, 1984.
7. G. Le Goualher, C. Barillot, and Y. Bizais. Modeling cortical sulci using active ribbons. Int. J. Pattern Recognit. Artific. Intell., 11(8):1295–1315, 1997.
8. G. Le Goualher, D. L. Collins, C. Barillot, and A. C. Evans. Automatic identification of cortical sulci using a 3D probabilistic atlas. In MICCAI’98, MIT, LNCS-1496, pages 509–518. Springer Verlag, 1998.
9. G. Le Goualher, E. Procyk, D. L. Collins, R. Venugopal, C. Barillot, and A. C. Evans. Automated extraction and variability analysis of sulcal neuroanatomy. IEEE Medical Imaging, 18(3):206–217, 1999.
10. G. Lohmann and Y von Cramon. Automatic detection and labelling of the human brain cortical folds in MR data sets. In ECCV, pages 369–381, 1998.
11. A. Manceaux-Demiau, J.-F. Mangin, J. Regis, O. Pizzato, and V. Frouin. Differential features of cortical folds. In Springer-Verlag, editor, CVRMED/MRCAS, Grenoble, LNCS-1205, pages 439–448, 1997.
12. J.-F. Mangin. Entropy minimization for automatic correction of intensity nonuniformity. In MMBIA, South Carolina, pages 162–169, 2000.
13. J.-F. Mangin, O. Coulon, and V. Frouin. Robust brain segmentation using histogram scale-space analysis and mathematical morphology. In MICCAI’98, MIT, LNCS-1496, pages 1230–1241. Springer Verlag, 1998.
14. J.-F. Mangin, V. Frouin, I. Bloch, J. Regis, and J. López-Krahe. From 3D MR images to structural representations of the cortex topography using topology preserving deformations. J. Mathematical Imaging and Vision, 5(4):297–318, 1995.
15. J.-F. Mangin, J. Regis, I. Bloch, V. Frouin, Y. Samson, and J. Lopez-Krahe. A Markovian random field based random graph modelling the human cortical topography. In CVRMed, Nice, LNCS-905, pages 177–183. Springer-Verlag, 1995.
16. J.-F. Mangin, J. Régis, and V. Frouin. Shape bottlenecks and conservative flow systems. In MMBIA, San Francisco, pages 319–328, 1996.
17. M. Ono, S. Kubik, and C. D. Abernethey. Atlas of the Cerebral Sulci. Georg Thieme Verlag, 1990.
18. J. Régis, J.-F. Mangin, V. Frouin, F. Sastre, J. C. Peragut, and Y. Samson. Generic model for the localization of the cerebral cortex and preoperative multimodal integration in epilepsy surgery. Stereotactic Functional Neurosurgery, 65:72–80, 1995.
19. M. E. Rettmann, C. Xu, D. L. Pham, and J. L. Prince. Automated segmentation of sulcal regions. In MICCAI’99, Cambridge, UK, LNCS-1679, Springer-Verlag, pages 158–167, 1999.
20. D. Rivière, J.-F. Mangin, J.-M. Martinez, F. Chavand, and V. Frouin. Neural network based learning of local compatibilities for segment grouping. In SSPR’98, LNCS-1451, pages 349–358. Springer Verlag, 1998.
21. N. Royackkers, M. Desvignes, H. Fawal, and M. Revenu. Detection and statistical analysis of human cortical sulci. NeuroImage, 10:625–641, 1999.
22. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error backpropagation, pages 318–362. MIT Press, 1986.
23. P. M. Thompson, R. P. Woods, M. S. Mega, and A. W. Toga. Mathematical/ computational challenges in creating deformable and probabilistic atlases of the human brain. Human Brain Mapping, 9:81–92, 2000.
24. M. Vaillant and C. Davatzikos. Finding parametric representations of the cortical sulci using active contour model. Medical Image Analysis, 1(4):295–315, 1997.
25. X. Zeng, L. H. Staib, R. T. Schultz, H. Tagare, L. Win, and J. S. Duncan. A new approach to 3D sulcal ribbon finding from MR images. In MICCAI’99, Cambridge, UK, LNCS-1679, Springer-Verlag, pages 148–157, 1999.