Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis

Medical Image Analysis

Volumn 6, Issue 2, 2002, Pages 163-179

  Rey, David ^a   Subsol, Gérard ^b   Delingette, Hervé ^a   Ayache, Nicholas ^a  

^a INRIA   (France)

^b LIA/CERI   (France)

Author keywords

3D medical imaging; Automatic detection and segmentation; Evolving processes; Multiple sclerosis; Vector field analysis; Vector operator

Indexed keywords

ANALYTIC METHOD; ARTICLE; AUTOANALYSIS; BRAIN SCINTISCANNING; CONTROLLED STUDY; DIAGNOSTIC IMAGING; IMAGE ANALYSIS; MULTIPLE SCLEROSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; THREE DIMENSIONAL IMAGING; VALIDATION PROCESS;

AUTOMATION; BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MULTIPLE SCLEROSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 0036624489     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1361-8415(02)00056-7     Document Type: Article

References (29)

1. Bello, F., Colchester, A., 1998. Measuring global and local spatial correspondence using information theory. In: Wells, W.M., Colchester, A., Delp, S. (Eds.), The First International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'98, Boston. Lecture Notes in Computer Science, Vol. 1496, pp. 964-973.
2. Bro-Nielsen, M., 1997. Medical image registration and surgery simulation. PhD thesis, IMM (electronic version: http://www.imm.dtu.dk/documents/users/bro/phd.html).
3. Brown, L.G., 1992. A survey of image registration techniques. ACM Comput. Surv. 24 (4), 325-376.
4. Cachier, P., Pennec, X., 2000. 3D non-rigid registration by gradient descent on a Gaussian-windowed similarity measure using convolutions. In: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'00), Hilton Head Island, SC, pp. 182-189.
5. Cachier, P., Rey, D., 2000. Symmetrization of the non-rigid registration problem using inversion-invariant energies: application to multiple sclerosis. In: Delp, S.L., DiGioia, A.M., Jaramaz, B. (Eds.), The Third International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'00, Pittsburgh. Lecture Notes in Computer Science, Vol. 1935, pp. 472-481.
6. Cachier, P., Pennec, X., Ayache, N., 1999. Fast non-rigid matching by gradient descent: study and improvements of the demons algorithm. Technical Report 3706, INRIA (electronic version: http://www.inria.fr/RRRT/RR-3706.html).
7. Calmon, G., Roberts, N., Eldridge, R, Thirion, J.-P., 1998. Automatic quantification of changes in the volume of brain structures. In: Wells, WM., Colchester, A., Delp, S. (Eds.), The First International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'98, Boston. Lecture Notes in Computer Science, Vol. 1496, pp. 761-769.
8. Davatzikos, C., Vaillant, M., Resnick, S., Prince, J.L., Letovsky, S., Bryan, R.N., 1996. Morphological analysis of brain structures using spatial normalization. In: Höhne, K.H., Kikinis, R. (Eds.), Visualization in Biomedical Computing. Lecture Notes in Computer Science, Vol. 1131. Springer, Hamburg, pp. 355-360, electronic version: http://iacl.ece.jhu.edu/_prince/jlp_pubs.html.
9. Gerig, G., Welti, D., Guttmann, C.R.G., Colchester, A.C.F., Székely, G., 2000. Exploring the discrimination power of the time domain for segmentation and characterization of lesions in serial MR data. Medical Image Analysis 4 (1), 31-42.
10. Guttmann, C.R.G., Kikinis, R., Anderson, M.C., Jakab, M., Warfield, S.K., Kiliany, R.J., Weiner, H.L., Jolesz, F.A., 1999. Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J. Magn. Reson. Imaging 9, 509-518.
11. Hajnal, J.V., Saeed, N., Oatridge, A., Williams, E.J., Young, I.R., Bydder, G., 1995. Detection of subtle brain changes using subvoxel registration and subtraction of serial MR images. J. Comput. Assist. Tomogr. 19 (5), 677-691.
12. Lebrun-Frénay, C., Rey, D., Malandain, G., Chanalet, S., Ayache, N., Chatel, M., 2000. Detection and quantification of MS lesions MRI variations by mathematical differential operators. In: 16th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Toulouse, France. Revue Neurologique (Supplement), Vol. 156.
13. Lemieux, L., Wiesmann, U.C., Moran, N.F., Fish, D.R., Shorvon, S.D., 1998. The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization. Medical Image Analysis 3 (2), 227-242.
14. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P, 1997. Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16 (2), 187-198.
15. Montagnat, J., Delingette, H., 1998. Globally constrained deformable models for 3D object reconstruction. Signal Processing 71, 173-186.
16. Pennec, X., 1996. L'incertitude dans les problèmes de reconnaissance et de recalage: application en imagerie médicale et biologie moléculaire. PhD Thesis.
17. Pennec, X., Thirion, J.-P., 1997. A framework for uncertainty and validation of 3D registration methods based on points and frames. Int. J. Comput. Vis. 25 (3), 203-229, electronic version: http://www.inria.fr/epidaure/personnel/pennec/Publications.html.
18. Prima, S., Thirion, J.-P., Subsol, G., Roberts, N., 1998. Automatic analysis of normal brain dissymmetry of males and females in MR images. In: Wells, W.M., Colchester, A., Delp, S. (Eds.), The First International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'98, Boston. Lecture Notes in Computer Science, Vol. 1496, pp. 770-779.
19. Rey, D., Subsol, G., Delingette, H., Ayache, N., 1998. Automatic detection and segmentation of evolving processes in 3D medical images: application to multiple sclerosis. Technical Report 3559, INRIA (electronic version: http://www.inria.fr/RRRT/RR-3559.html).
20. Rey, D., Delingette, H., Subsol, G., Ayache, N., 1999. Using continuum mechanics operators for detection and quantification of evolving processes in 3D medical images. In: EUROMECH406, Warsaw, Poland, pp. 185-188, electronic version (abstract): http://at.yorku.ca/cgi-bin/amca/cacp-11.
21. Rey, D., Lebrun-Frénay, C., Malandain, G., Chanalet, S., Ayache, N., Chatel, M., 2000. A new method to detect and quantify evolving MS lesions by mathematical operators. Neurology (Supplement) 54 (7), 123.
22. Roche, A., Malandain, G., Ayache, N., 2000. Unifying maximum likelihood approaches in medical image registration. Int. J. Imaging Syst. Technol. 11, 71-80.
23. Thirion, J.-P., 1996. New feature points based on geometric invariants for 3D image registration. Int. J. Comput. Vis. 18 (2), 121-137; electronic version: http://www.inria.fr/RRRT/RR-1901.html.
24. Thirion, J.-R, 1998. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis 2 (3), 243-260; electronic version: http://www.inria.fr/RRRT/RR-2547.html.
25. Thirion, J.-P., Calmon, G., 1999. Deformation analysis to detect and quantify active lesions in 3D medical image sequences. IEEE Trans. Med. Imaging 18 (5), 429-441; electronic version: http://www.inria.fr/RRRT/RR-3101.html.
26. Warfield, S., Dengler, J., Zaers, J., Guttmann, C.R.G., Wells, W.M., Ettinger, G.J., Hiller, J., Kikinis, R., 1995. Automatic identification of grey matter structure from MRI to improve the segmentation of white matter lesions. J. Image Guided Surg. 1 (6), 326-338.
27. Weiss, J.A., Maker, B.N., Govindjee, S., 1997. Finite element implementation of incompressible transversely isotropic hyperelasticity. Comput. Methods Appl. Mech. Eng. 135, 107-128.
28. Welti, D., Gerig, G., Radü, E.-W., Kappos, L., Székely, G., 2001. Spatio-temporal segmentation of active multiple sclerosis lesions in serial MRI data. In: Insana, MR, Leahy, R.M. (Eds.), 17th International Conference on Information Processing in Medical Imaging (IPMI'O1), Davis, CA. Lecture Notes in Computer Science, Vol. 2082, pp. 438-445.
29. Zijdenbos, A., Forghani, R., Evans, A., 1998. Automatic quantification of MS lesions in 3D MRI brain data sets: validation of insect. In: Wells, W.M., Colchester, A., Delp, S. (Eds.), The First International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'98, Boston. Lecture Notes in Computer Science, Vol. 1496, pp. 439-448.