Statistical cerebrovascular segmentation in three-dimensional rotational angiography based on maximum intensity projections

Medical Physics

Volumn 32, Issue 9, 2005, Pages 3017-3028

  Gan, Rui ^a   Wong, Wilbur C K ^a   Chung, Albert C S ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

3D rotational angiography (3D RA); Expectation maximization (EM) algorithm; Finite mixture model (FMM); Image segmentation; Maximum intensity projection (MIP)

Indexed keywords

ANGIOGRAPHY; COMPUTATIONAL EFFICIENCY; IMAGE SEGMENTATION; ITERATIVE METHODS; MAXIMUM PRINCIPLE; MEDICAL IMAGING; STATISTICAL METHODS;

3D ROTATIONAL ANGIOGRAPHY; CEREBROVASCULAR SEGMENTATION; COMPUTATIONALLY EFFICIENT; EXPECTATION-MAXIMIZATION ALGORITHMS; FINITE MIXTURE MODELING; MAXIMUM INTENSITY PROJECTION; MORPHOLOGICAL INFORMATION; THREE DIMENSIONAL ROTATIONAL ANGIOGRAPHY;

THREE DIMENSIONAL COMPUTER GRAPHICS;

ACCURACY; ANGIOGRAPHY; ARTICLE; BRAIN BLOOD VESSEL; IMAGE PROCESSING; IMAGE QUALITY; MORPHOLOGY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; STATISTICAL MODEL; TECHNIQUE; THREE DIMENSIONAL IMAGING; THREE DIMENSIONAL ROTATIONAL ANGIOGRAPHY;

EID: 25644444050     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.2001820     Document Type: Article

References (29)

1. K. Krissian, G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset, "Model-based detection of tubular structures in 3D images," Comput. Vis. Image Underst. 80, 130-171 (2000).
2. S. Aylward and E. Bullitt, "Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction," IEEE Trans. Med. Imaging 21, 61-75 (2002).
3. W. J. Niessen, C. M. van Bemmel, A. F. Frangi, M. J. A. Siers, and O. Wink, "Model-based segmentation of cardiac and vascular images," in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), 2002, pp. 22-25.
4. C. F. Westin, L. M. Lorigo, O. D. Faugeras, W. E. L. Grimson, S. Dawson, A. Norbash, and R. Kikinis, "Segmentation by adaptive geodesic active contours," in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'00), Pittsburgh, Pennsylvania, October 11-14, 2000, pp. 266-275.
5. L. M. Lorigo, O. D. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C. F. Westin, "CURVES: Curve evolution for vessel segmentation," Med. Image Anal 5, 195-206 (2001).
6. B. Chapman, J. Stapelton, and D. Parker, "Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the mip z-buffer: accuracy of the zbs algorithm," Med. Image Anal 8, 113-126 (2004).
7. D. L. Wilson and J. A. Noble, "An adaptive segmentation algorithm for time-of-flight MRA data," IEEE Trans. Med. Imaging 18, 938-945 (1999).
8. D. L. Wilson and J. A. Noble, "Segmentation of cerebral vessels and aneurysms from MR angiography data," in Proceedings of International Conference on Information Processing in Medical Imaging (IPMI'97), Pourtney, Vermont, June 9-13, 1997, pp. 423-428.
9. M. S. Hassouna, A. A. Farag, S. Hushek, and T. Moriarty, "Statistical-based approach for extracting 3D blood vessels from TOF-MRA data," in Proceedings of International Conference on Medical Image Computing and Computer- Assisted Intervention (MICCAI'03), Montreal, Canada, November 15-18, 2003, pp. 680-687.
10. A. C. S. Chung, J. A. Noble, and P. Summers, "Fusing speed and phase information for vascular segmentation of phase contrast MR angiograms," Med. Image Anal 6, 109-128 (2002).
11. A. C. S. Chung, J. A. Noble, and P. Summers, "Vascular segmentation of phase contrast magnetic resonance angiograms based on statistical mixture modeling and local phase coherence," IEEE Trans. Med. Imaging 23, 1490-1507 (2004).
12. M. S. HassounaC. B. Sites, A. A. Farag, S. Hushek, and T. Moriarty, "Statistical cerebrovascular segmentation for phase-contrast MRA data," in Proceedings of International Conference on Biomedical Engineering (ICBE'02), Cairo, Egypt, December, 2002, pp. 32-37.
13. J. Moret, R. Kemkers, J. O. de Beek, R. Koppe, E. Klotz, and M. Grass, "3D rotational angiography: Clinical value in endovascular treatment," Med. Mundi 42, 8-14 (1998).
14. R. Kemkers, J. O. de Beek, H. Aerts, R. Koppe, E. Klotz, M. Grass, and J. Moret, "3D-rotational angiography: First clinical application with use of a standard philips C-arm system," in Proceedings of the 12th International Symposium and Exhibition, Computer Assisted Radiology and Surgery (CAR'98), Tokyo, Japan, June 24-27, 1998, pp. 182-187.
15. M. Grass, R. Koppe, E. Klotz, R. Proksa, M. H. Kuhn, H. Aerts, J. O. de Beek, and R. Kemkers, "Three-dimensional reconstruction of high contrast objects using C-arm image intensifier projection data," Comput. Med. Imaging Graph. 23, 311-321 (1999).
16. Y. Trousset, R. Vaillant, L. Launay, J.-M. Obadia, N. Pivet, R. Anxionnat, and L. Licard, "A fully automated system for three-dimensional X-ray angiography," in Proceedings of the 13th International Symposium and Exhibition, Computer Assisted Radiology and Surgery (CAR'99). Paris, France, June 23-26, 1999, pp. 39-43.
17. E. Meijering, W. Niessen, J. Weickert, and M. Viergever, "Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: Results of an in-vitro evaluation," Med. Image Anal 6, 215-233 (2002).
18. R. Gan, W. C. K. Wong, A. C. S. Chung, and S. C. H. Yu, "Statistical cerebrovascular segmentation in three-dimensional rotational angiography based on maximum intensity projections," in Proceedings of the 18th International Congress and Exhibition, Computer Assisted Radiology and Surgery (CARS 2004), Paris, France, June 23-26, 2004, pp. 195-200.
19. J. K. Fwu and P. M. Djurić, "EM algorithm for image segmentation initialized by a tree structure," IEEE Trans. Image Process. 6, 349-352 (1997).
20. W. M. Wells, R. Kikinis, W. E. L. Crimson, and F. Jolesz, "Adaptive segmentation of MRI data," IEEE Trans. Med. Imaging 15, 429-442 (1996).
21. C. Bishop, Neural Networks for Pattern Recognition (Clarendon. Oxford, 1995).
22. A. H. Andersen and J. E. Kirsch, "Analysis of noise in phase contrast MR imaging," Med. Phys. 23, 857-869 (1996).
23. S. C, Horii and Y. Kim, Handbook of Medical Imaging, Volume 3: Display and PACS (SPIE-The International Society for Optical Engineering, Bellingham, WA, 2000), Vol. 3.
24. C. W. Bakal, J. E. Silberzweig, J. Cynamon, and S. Sprayregen, Vascular and International Radiology: Principles and Practice (Thieme Medical Publishers, Inc., New York, 2002).
25. S. M. Hertz, R. A. Baum, R. S. Owen, G. A. Holland, D. R. Logan, and J. P. Carpenter, "Comparison of magnetic resonance angiography and contrast arteriography in peripheral arterial stenosis," Am. J. Surg. 166, 112-116 (1993).
26. S. Napel, M. P. Marks, G. D. Rubin, M. D. Dake, C. H. McDonnell, S. M. Song, D. R. Enzmann, and R. B. Jeffrey, "CT angiography with spiral CT and maximum intensity projection," Radiology 185, 607-610 (1992).
27. A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, "Morphometric analysis of white matter lesions in MR images: Method and validation," IEEE Trans. Med. Imaging 13, 716-724 (1994).
28. J. J. Bartko, "Measurement and reliability: statistical thinking considerations," Schizophr Bull. 17, 483-489 (1991).
29. S. K. Warfield, K. H. Zou, and W. M. Wells III, "Validation of image segmentation and expert quality with an expectation-maximization algorithm," in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02), Tokyo, Japan, September 25-28, 2002, pp. 298-306.