Detection and quantification of line and sheet structures in 3-D images

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

Volumn 1935, Issue , 2000, Pages 154-165

  Sato, Yoshinobu ^a   Tamura, Shinichi ^a  

^a OSAKA UNIVERSITY GRADUATE SCHOOL OF MEDICINE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD VESSELS; CARTILAGE; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MEDICAL COMPUTING; MEDICAL IMAGING;

ANATOMICAL STRUCTURES; ARTICULAR CARTILAGES; CARTILAGE THICKNESS; DETECTION AND QUANTIFICATIONS; DETECTION PERFORMANCE; DIRECTIONAL DERIVATIVE; INTENSITY STRUCTURE; VARIABLE CROSS SECTION;

COMPUTERIZED TOMOGRAPHY;

EID: 84945543493     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-40899-4_16     Document Type: Conference Paper

References (13)

1. B. G. Brown, E. L. Bolson, M. Frimer, et al.: Quantitative coronary arteriography, Circulation, 55, 329-337, (1977).
2. J. H. C. Reiber, C. J. Kooijman, C. J. Slager, et al.: Coronary artery dimensions from cineangiograms — methodology and validation of a computer-assisted analysis procedure, IEEE Transactions on Medical Imaging, 3(3), 131-141 (1984).
3. Y. Sato, T. Araki, M. Hanayama, et al.: A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition, IEEE Transactions on Medical Imaging, 17(1), 121-137 (1998).
4. C. Steger: An unbiased detector of curvilinear structures: IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(2), 113-125 (1998).
5. T. M. Koller, G. Gerig, G. Szekely, et al.: Multiscale detection of curvilinear structures in 2-D and 3-D image data, Proc. Fifth International Conference on Computer Vision (ICCV’95), Boston, MA, 864-869 (1995).
6. B. S. Morse, S. M. Pizer, and A. Liu: Multiscale medical analysis of medical images: Image and Vision Computing, 12(6): 327-338 (1994).
7. Y. Sato, S. Nakajima, N. Shiraga, et al.: Three dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, Medical Image Analysis, 2(2), 143-168 (1998).
8. K. Krissian, G. Malandain, N. Ayache, et al.: Model-based multiscale detection of 3D vessels, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’98), Santa Barbara, CA, (1998).
9. Y. Sato, C-F Westin, A. Bhalerao, et al.: Tissue classification based on 3D local intensity structures for volume rendering, IEEE Transactions on Visualization and Computer Graphics, 6(2), 160-180 (2000).
10. T. O’Donnell, A. Gupta, and T. Boult: A new model for the recovery of cylindrical structures from medical image data, Lecture Notes in Computer Science (LNCS), 1205 (CVRMed-MRCAS’97), 223-232 (1997).
11. A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, et al.: Model-based quantification of 3-D magnetic resonance angiographic images, IEEE Transactions on Medical Imaging, 18(10), 946-956, (1999)
12. N. Nakanishi, H. Tanaka, T. Nishii, et al.: MR evaluation of the articular cartilage of the femoral head during traction, Acta Radiologica, 40, 60-63 (1999).
13. Y. Sato, T. Kubota, K. Nakanishi, et al.: Three-dimensional reconstruction and quantification of hip joint cartilages from magnetic resonance images, Lecture Notes in Computer Science (LNCS), 1679 (MICCAI’99), 338-347 (1999).