Improved maximum a posteriori cortical segmentation by iterative relaxation of priors

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

Volumn 5762 LNCS, Issue PART 2, 2009, Pages 441-449

  Cardoso, Manuel Jorge ^a   Clarkson, Matthew J ^a   Ridgway, Gerard R ^a   Modat, Marc ^a   Fox, Nick C ^a   Ourselin, Sebastien ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LAPLACE EQUATION; MEDICAL COMPUTING; THICKNESS GAGES; THICKNESS MEASUREMENT;

ALZHEIMER'S; ALZHEIMER'S DISEASE; CEREBRAL CORTEX; DIGITAL PHANTOMS; EULERIAN FRAMEWORK; ITERATIVE MODIFICATIONS; ITERATIVE RELAXATIONS; LAGRANGIAN; LIMITED RESOLUTION; MAXIMUM A POSTERIORI; MR IMAGES; NEURODEGENERATIVE DISEASE; PARTIAL VOLUME EFFECT; PRIOR INFORMATION; REDUCTION OF ERRORS; SUB-VOXEL ACCURACY; TEMPORAL EVOLUTION; THICKNESS ESTIMATION; TISSUE SEGMENTATION; VOXEL-BASED APPROACHES;

DIAGNOSIS;

EID: 77952262201     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04271-3_54     Document Type: Conference Paper

References (12)

1. Fischl, B., Dale, A.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. PNAS 97, 11044-11049 (2000)
2. Hutton, C., Vita, E.D., Ashburner, J., Deichmann, R., Turner, R.: Voxel-based cortical thickness measurements in MRI. Neuroimage 40, 1701-1710 (2008)
3. Lohmann, G., Preul, C., Hund-Georgiadis, M.: Morphology-based cortical thickness estimation. In: IPMI 2003, pp. 89-100 (2003)
4. Acosta, O., Bourgeat, P., Fripp, J., Bonner, E., Ourselin, S., Salvado, O.: Automatic delineation of sulci and improved partial volume classification for accurate 3D voxel-based cortical thickness estimation from MR. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 253- 261. Springer, Heidelberg (2008)
5. Wells, M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A.: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15, 429-442 (1996)
6. Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions onMedical Imaging 18, 897-908 (1999) (Pubitemid 32211105)
7. Ashburner, J., Friston, K.: Unified segmentation. Neuroimage 26, 839-851 (2005)
8. Klauschen, F., Goldman, A., Barra, V., Meyer-Lindenberg, A., Lundervold, A.: Evaluation of automated brain MR image segmentation and volumetry methods. Human Brain Mapping 30, 1310-1327 (2009)
9. Smith, S., Jenkinson, M., Woolrich, M., Beckmann, C., Behrens, T., Bannister, P., DeLuca, M., Drobnjak, I., Flitney, D.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, 208-219 (2004) (Pubitemid 39421720)
10. Modat, M., Taylor, Z.A., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using the normalised mutual information gradient and graphics processing units. In: High-Performance MICCAI workshop (2008)
11. Schott, J., Price, S., Frost, C., Rossor, M., Fox, N.: Measuring atrophy in Alzheimer disease: a serial MRI study over 6 and 12 months. Neurology 65(1), 119-124 (2005) (Pubitemid 40967786)
12. Tzourio-Mazoyer, N., Landeau, B., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI. Neuroimage 15, 273-289 (2002) (Pubitemid 34852226)