Automatic detection of sulcal bottom lines in MR images of the human brain

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

Volumn 1230, Issue , 1997, Pages 369-374

  Lohmann, Gabriele ^a   Kruggel, Frithjof ^a   Von Cramon, D Yves ^a  

^a MAX PLANCK INSTITUTE FOR HUMAN COGNITIVE AND BRAIN SCIENCES   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING;

AUTOMATIC DETECTION; AUTOMATIC PROCEDURES; BOTTOM LINES; DISTANCE TRANSFORMS; HUMAN BRAIN; MORPHOLOGICAL OPERATOR; MR IMAGES;

MEDICAL IMAGING;

EID: 84956857130     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63046-5_28     Document Type: Conference Paper

References (17)

1. M. Ono, S. Knbik, C.D. Abernathy. Atlas of the cerebral sulci. Georg Thieme Verlag, Stuttgart, New York, 1990.
2. T. Paus, F. Tomaiuolo, N. Otaky, D. MacDonald, M. Petrides, J. Atlas, R. Morris, A.C. Evans. Human cingulate and paraeingula.te sulci: pattern, variability, asymmetry, and probabilistic map. Cerebral Cortex, 6:207-214, Mar/Apt 1996.
3. P.M. Thompson, C. Schwartz, R. T. Lin, A.A. Khan, A. T. Toga. Three-dimensional statistical analysis of sulcal variability in the human brain. The Journal of Neuroscience, 16(13):4261-4274, 1996.
4. J. Declerck, G. Subsol, J.-P. Thirion, N. Ayache. Automatic retrieval of a.natomical structures in 3D medical images. In N. Ayache, editor, Computer Vision, Virtual Reality and Robotics in Medicine, pages 153-162, Nice, France, April 1995. Springer Lecture Notes, 905.
5. A. GuSziec, N. Ayache. Smoothing and matching of 3-d space curves. International Journal of Computer Vision., 12(1):79-104, 1994.
6. O. Monga, S. Benayoun. Using partial derivatives of 3d images to extract typical surface features. Computer vision and image understanding, 61(2):171-189, March 1995.
7. J.P. Thirion, A. Gourdon. The marching lines algorithm: new results and proofs. Technical Report No. 1881, April 1993.
8. R. L. Ogniewicz, G. Szekely, O. Kübler. Detection of prominent boundary points based on structural characteristics of the hierarchic mediM axis transform. In H. (Hrsg.) PSppl, S. It.; Handels, editor, 15. DAGM-Symposium, Mustererkennung 1993, pages 321-331. Springer Verlag, 1993.
9. Yaorong Ge, J. Michael Fitzgerald, Benoit M. Dawant, Jun Bao, Robert M. Kessler, Richard A. Margolin. Accurate localization of cortical convolutions in MR brain images. IEEE Transactions on Medical Imaging, 15(4):418-428, Aug. 1996.
10. M. Valliant, C. Davatzikos, R.N. Bryan. Finding 3D parametric representations of the deep cortical folds. In Proc. Mathematical Methods in biomedical image analysis (MMBL4 96), pages 151-157, San Francisco, CA, June 1996. IEEE Computer Society.
11. G. Lohmann, F. Kruggel. Extracting lines of maximal depth fi'om MR I images of the human brain. In Proe. Intern. Conj. on Pattern Recognition, Wien, Aug. 1996.
12. P. W. Verbeek, L. Dorst, B. J. H. Verwer, F. C. A. Groen. Collision avoidance and path finding through constrained distance transformation in robot state space. In Proc. Intelligent Autonomous Systems, pages 634-641, Amsterdam, The Netherlands, Dec. 1986.
13. G. Borgefors. Distance tra.nsforms in arbitrary dimensions. Computer Vision, Graphics, and Image Processing, 27:321-345, 1984.
14. T. C. Lee, R. L. Kashyap, C. N. Chu. Building skeleton models via 3-d medial surface axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994.
15. R.L. Ogniewicz, M. Ilg. Voronoi skeletons: theory and applications. In Proc. Computer Vision and Pattern Recognition (CVPR 92), pages 63-69. Computer Society Press, 1992.
16. Y.F. Tsao, K.S. Fu. A parallel thinning algorithm for 3d pictures. Computer Graphics linage Proc., 17:315-331, 1981.
17. G. Malandain. personal communication, July 1996.