A combined region growing and deformable model method for extraction of closed surfaces in 3D CT and MRI scans

Computerized Medical Imaging and Graphics

Volumn 33, Issue 5, 2009, Pages 369-376

  del Fresno, M ^a   Venere M ^a   Clausse, A ^a  

^a UNIVERSIDAD NACIONAL DEL CENTRO DE LA PROVINCIA DE BUENOS AIRES   (Argentina)

Author keywords

Deformable surface models; Hybrid methods; Image segmentation; MRI; Region growing

Indexed keywords

3-D VISUALIZATIONS; 3D IMAGE SEGMENTATIONS; 3D MEDICAL IMAGES; ANATOMICAL OBJECTS; ANATOMICAL STRUCTURES; BRAIN DATUM; CLOSED SURFACES; CT DATUM; DEFORMABLE MODELS; DEFORMABLE SURFACE MODELS; EXCELLENT PERFORMANCE; HEAD STRUCTURES; HIGH QUALITIES; HIGH-QUALITY SEGMENTATIONS; HYBRID METHODS; IMAGE ARTIFACTS; INTEGRATED STRATEGIES; INTERNAL SURFACES; MRI; MRI SCANS; NOISE-INTERFERENCES; OBJECT STRUCTURES; OVERLAP MEASURES; PRACTICAL ISSUES; REGION GROWING; SEGMENTATION RESULTS; WHITE MATTERS;

APPROXIMATION ALGORITHMS; COMPUTATIONAL COMPLEXITY; DEFORMATION; DIGITAL IMAGE STORAGE; HYBRID SENSORS; IMAGE SEGMENTATION; MEDICAL IMAGING; THREE DIMENSIONAL COMPUTER GRAPHICS;

THREE DIMENSIONAL;

ARTICLE; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; COMPUTER ANALYSIS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; THREE DIMENSIONAL IMAGING; WHITE MATTER;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; HUMANS; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, ANATOMIC; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; TOMOGRAPHY, X-RAY COMPUTED;

EID: 67349195754     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2009.03.002     Document Type: Article

References (35)

1. Duncan J., and Ayache N. Medical image analysis: progress over two decades and challenges ahead. IEEE Transactions on Pattern Recognition and Machine Intelligence 22 1 (2000) 85-106
2. Imielinska C., and Molholt P. Incorporating 3D virtual anatomy into the medical curriculum. Communications of the ACM 48 2. (2005) 49-54
3. Pham D., Xu C., and Prince J. A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering 2 (2000) 315-337
4. Lucchese L., and Mitra S. Color image segmentation: a state-of-the-art survey, image processing, vision, and pattern recognition. The Indian National Science Academy 67 2 (2001) 207-221
5. Castleman K. Digital image processing (1996), Prentice Hall, NJ
6. Kass M., Witkin A., and Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision 1 (1988) 321-331
7. Adams R., and Bischof L. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16 6 (1994) 641-647
8. Fan J., Yau D., Elmagarmid A., and Aref W. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing 10 10 (2001) 1454-1466
9. Pohle R., Behlau T., and Töennies K. Segmentation of 3-D medical image data sets with a combination of region based initial segmentation and actives surfaces. Proceedings of SPIE Medical Imaging 5032 (2003) 1225-1232
10. Freixenet J, Muñoz X, Raba D, Marti J, Cufi X. Yet another survey on image segmentation: region and boundary information integration, ECCV 2002, LNCS 2352, Springer-Verlang Berlin Heidelberg; 408-422.
11. Yu Z., Zhu Y., Yang J., and Zhu Y. A hybrid region-boundary model for cerebral cortical segmentation in MRI. Computerized Medical Imaging and Graphics 30 (2006) 197-208
12. Lorensen W., and Cline H. Marching cubes a high resolution 3D surface reconstruction algorithm. Computer Graphics 21 4 (1987) 163-169
13. Strauss E., Jimenez W., Giraldi G., Silva R., and Oliveira A. A semi-automatic surface reconstruction framework based on T-surfaces and isosurface extraction methods. XV Brazilian symposium on computer graphics and image processing (SIBGRAPI '02) (2002) 24
14. Chen T., and Metaxas D. A hybrid framework for 3D medical image segmentation. Medical Image Analysis 9 (2005) 547-565
15. Cohen L. On active contour models and balloons. CVGIP: Image Understanding 53 2 (1991) 211-218
16. Xu C., and Prince J. Snakes, shapes and gradient vector flow. IEEE Transactions on Image Processing 7 (1998) 359-369
17. McInerney T., and Terzopoulos D. Topologically adaptable snakes. Proc. of the fifth int. conf. on computer vision. Cambridge, MA, USA (1995) 840-845
18. McInerney T., and Terzopoulos D. Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Transactions on Medical Imaging 18 10 (1999) 840-850
19. Lin Z., Jin J., and Talbot H. Unseeded region growing for 3D image segmentation. ACM International Conference Proceeding Series 9 (2000) 31-37
20. Olabarriaga S., and Smeulders A. Interaction in the segmentation of medical images: a survey. Medical Image Analysis 5 (2001) 127-142
21. Cohen M., Painter J., Mehta M., and Ma K. Volume seedlings. Proceedings of the ACM symposium on interactive 3D graphics (1992) 139-145
22. Kim D., Chung S., and Park J. Automatic navigation path generation based on two-phase adaptive region-growing algorithm for virtual angioscopy. Medical Engineering and Physics 28 (2006) 339-347
23. Chen Z., and Molloi S. Automatic 3D vascular tree construction in CT angiography. Computerized Medical Imaging and Graphics 27 (2003) 469-479
24. Hahn H., Preim B., Selle D., and Peitgen H. Visualization and interaction techniques for the exploration of vascular structures. Proceedings of visualization conference (2001) 395-402
25. Kiraly A., Higgins W., McLennan G., Hoffman E., and Reinhardt J. Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. Academic Radiology 9 10 (2002) 1153-1168
26. Zhou X., Hayashi T., Hara T., Fujita H., Yokohama R., Kiryu T., et al. Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images. Computerized Medical Imaging and Graphics 30 (2006) 299-313
27. Thiran J., Warscotte V., and Macq B. A queue-based region growing algorithm for accurate segmentation of multi-dimensional digital images. Signal Processing 60 (1997) 1-10
28. Xu C., Pham D., Rettmann M., Yu D., and Prince J. Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Transactions on Medical Imaging 18 6 (1999) 467-480
29. Hojjatoleslami S., and Kruggel F. Segmentation of large brain lesions. IEEE Transactions on Medical Imaging 20 7 (2001) 666-669
30. Taubin G. A signal processing approach to fair surface design. Computer graphics proceedings (1995) 351-358
31. Amini A., Weymouth T., and Jain R. Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 9 (1990) 855-867
32. Cohen L., and Cohen I. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 11 (1993) 1131-1147
33. Collins D., Zijdenbos A., Kollokian V., Sled J., Kabani N., Holmes C., et al. Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging 17 3 (1998) 463-468
34. Pichon E., Tannenbaum A., and Kikinis R. A statistically based surface evolution method for medical image segmentation: presentation and validation. MICCAI 2 (2003) 711-720
35. Yoo T.S., Ackerman M.J., Lorensen W.E., Schroeder W., Chalana V., Aylward S., et al. Engineering and algorithm design for an image processing Api: a technical report on ITK-the Insight Toolkit. Studies in Health Technology and Informatics 85 (2000) 586-592