Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images

Applied Optics

Volumn 36, Issue 35, 1997, Pages 9269-9286

  Michael, Nevine ^a,b   Arrathoon, Raymond ^a  

^a Wayne State University   (United States)

^b Applied Intelligent Systems   (United States)

Author keywords

Digital image processing; Digital optical computing; Image segmentation; Magnetic resonance imaging; Mathematical morphology; Optoelectronic logic arrays

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; FIBER OPTICS; IMAGE SEGMENTATION; ITERATIVE METHODS; LOGIC CIRCUITS; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MORPHOLOGY; OPTICAL DATA PROCESSING; PARALLEL PROCESSING SYSTEMS;

DIGITAL OPTICAL COMPUTING; PROGRAMMABLE LOGIC ARRAY; WATERSHED TRANSFORM;

OPTOELECTRONIC DEVICES;

EID: 0031334092     PISSN: 1559128X     EISSN: 21553165     Source Type: Journal    
DOI: 10.1364/AO.36.009269     Document Type: Article

References (30)

1. J. Serra, Image Analysis and Mathematical Morphology (Academic, San Diego, Calif., 1982).
2. E. R. Dougherty, An Introduction to Morphological Image Processing (SPIE Optical Engineering Press, Bellingham, Wash., 1992).
3. R. Haralick and L. Shapiro, “Survey: image segmentation techniques,” Comput. Vision Graphics Image Process. 29(1), 100-132 (1985).
4. S. Beucher, “Segmentation tools in mathematical morphology,” in Image Algebra and Morphological Image Processing, P. D. Gader, ed., Proc. SPIE 1350, 70-84 (1990).
5. S. Beucher, “The watershed transformation applied to image segmentation,” in Proceedings of Scanning Microscopy (Scanning Microscopy International, Chicago, 111., 1992), pp. 299-314.
6. S. Beucher and F. Meyer, “The morphological approach to segmentation: the watershed transformation,” in Mathematical Morphology in Image Processing, E. R. Dougherty, ed. (Marcel Dekker, New York, 1993), pp. 433-481.
7. S. Beucher and C. Lantuejoul, “Use of watersheds in contour detection,” paper presented at International Workshop on Image Processing: Real Time Edge and Motion Detection/Estimation, CCETT/IRISA, Rennes, France, September 1979.
8. L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intel. 13(6), 583-598 (1991).
9. e Congres AFCET, Reconnaissance des Formes et Intelligence Artificielle (Association Francaise des Sciences et Technologies de l’Information et des Systemes, 1992), Vol. 2, pp. 847-859.
10. B. P. Dobrin, T. Viero, and M. Gabbouj, “Fast watershed algorithms: analysis and extensions,” in Nonlinear Image Processing V, E. R. Dougherty, J. T. Astola, and H. G. Longbotham, eds., Proc. SPIE 2180, 209-220 (1994).
11. R. Arrathoon, “Historical perspectives: optical crossbars and optical computing,” in Digital Optical Computing, R. Arrathoon, ed., Proc. SPIE 752, 2-11 (1987).
12. R. Arrathoon, “Fiber-optic programmable logic arrays,” in Optical Computing: Digital and Symbolic, R. Arrathoon, ed. (Marcel Dekker, New York, 1989), pp. 247-278.
13. F. Y. Shih and O. R. Mitchell, “Threshold decomposition of gray-scale morphology into binary morphology,” IEEE Trans. Pattern Anal. Mach. Intel. 11(1), 31-42 (1989).
14. L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE Trans. Image Process. 2(2), 176-201 (1993).
15. N. Michael and R. Arrathoon, “Optoelectronic pipeline architecture for morphological image processing,” Appl. Opt. 36(8), 1718-1725 (1997).
16. S. Beucher and M. Bilodeau, “Road segmentation and obstacle detection by a fast watershed transformation,” in Proceedings of the Intelligent Vehicles 1994 Symposium (Institute of Electrical and Electronics Engineers, New York, 1994), pp. 296-301.
17. L. Vincent, “Morphological image processing and network analysis of cornea endothelial cell images,” in Image Algebra and Morphological Image Processing III, P. D. Gader, E. R. Dougherty, and J. C. Serra, eds., Proc. SPIE 1769, 212-226 (1992).
18. N. Rougon and F. Preteux, “Quantitative automated analysis of cornea endothelial cell images,” in Image Algebra and Morphological Image Processing V, E. R. Dougherty, P. D. Gader, and M. Schmitt, eds., Proc. SPIE 2300, 133-144 (1994).
19. N. Michael and R. Arrathoon, “Optical PLA implementation for selected complex morphological algorithms,” in Hybrid Image and Signal Processing V, D. P. Casasent and A. G. Tescher, eds., Proc. SPIE 2751, 186-198 (1996).
20. D. N. Levin, X. Hu, and K. K. Tan, “Surface of the three-dimension MR images created with volume rendering,” Radiology 171, 277-280 (1989).
21. K. H. Hoehne, M. Bomans, A. Pommert, M. Reimer, U. Tiede, and G. Wiebecke, “Rendering tomographic volume data: ad equacy of methods for different modalities and organs,” in 3D Imaging in Medicine: Algorithms, Systems, Applications, K. H. Hoehne, H. Fuchs, and S. M. Pizer, eds. (SpringerVerlag, Berlin, 1990), pp. 197-215.
22. J. C. Bezdek, L. O. Hall, and L. P. Clarke, “Review of MR image segmentation techniques using pattern recognition,” Med. Phys. 20(4), 1033-1048 (1993).
23. L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, L. O. Hall, R. W. Thatcher, and M. S. Silbiger, “MRI segmentation: methods and applications,” Magn. Res onance Imaging 13(3), 343-368 (1995).
24. M. C. Clark, L. O. Hall, D. B. Goldgof, L. P. Clarke, R. P. Velthuizen, and M. S. Silbiger, “MRI segmentation using fuzzy clustering techniques,” IEEE Eng. Medicine Biol. 13(5), 730-742 (1994).
25. W. Connor and P. Diaz, “Morphological segmentation and 3-D rendering of the brain in magnetic resonance imaging,” in Image Algebra and Morphological Image Processing II, P. D. Gader and E. R. Dougherty, eds., Proc. SPIE 1568, 327-334 (1991).
26. M. E. Brummer, R. M. Mersereau, R. L. Eisner, and R. J. Lewine, “Automatic detection of brain contours in MRI data sets,” IEEE Trans. Medical Imaging 12(2), 153-166 (1993).
27. R. Acharya and Y. Ma, “Segmentation algorithm for cranial magnetic resonance images,” in Medical Imaging VI: Image Processing, M. H. Loew, ed., Proc. SPIE 1652, 50-61 (1992).
28. M. Bomans, K. H. Hoehne, U. Tiede, and M. Riemer, “3-D segmentation of MR images of the head for 3-D display,” IEEE Trans. Medical Imaging 9, 177-183 (1990).
29. KHOROS is an image processing system developed by Khoral, Inc., Albequerque, NM.
30. J. Barrera, G. J. F. Banon, and R. Lotufo, “Mathematical morphology toolbox for the Khoros system,” in Image Algebra and Morphological Image Processing V, E. R. Dougherty, P. D. Gader, and M. Schmitt, eds., Proc. SPIE 2300, 241-252 (1994).