OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 36, Iss. 35 — Dec. 10, 1997
  • pp: 9269–9286

Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images

Nevine Michael and Raymond Arrathoon  »View Author Affiliations


Applied Optics, Vol. 36, Issue 35, pp. 9269-9286 (1997)
http://dx.doi.org/10.1364/AO.36.009269


View Full Text Article

Enhanced HTML    Acrobat PDF (2706 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

An optoelectronic implementation for the morphological watershed transform is proposed. Fiber-optic programmable logic arrays are used in the implementation because of their high fan factors at high clock speeds. Image segmentation is one of the main applications of the watershed transform. Based on the optoelectronic implementation, an algorithm for the segmentation of axial magnetic resonance (MR) head images to extract information on brain matter is presented. Simulation results for the different steps of the segmentation process are presented.

© 1997 Optical Society of America

History
Original Manuscript: May 5, 1997
Revised Manuscript: July 17, 1997
Published: December 10, 1997

Citation
Nevine Michael and Raymond Arrathoon, "Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images," Appl. Opt. 36, 9269-9286 (1997)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-36-35-9269


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  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, L. Shapiro, “Survey: image segmentation techniques,” Comput. Vision Graphics Image Process. 29(1), 100–132 (1985). [CrossRef]
  4. S. Beucher, “Segmentation tools in mathematical morphology,” in Image Algebra and Morphological Image Processing, P. D. Gader, ed., Proc. SPIE1350, 70–84 (1990).
  5. S. Beucher, “The watershed transformation applied to image segmentation,” in Proceedings of Scanning Microscopy (Scanning Microscopy International, Chicago, Ill., 1992), pp. 299–314.
  6. S. Beucher, 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, 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, P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intel. 13(6), 583–598 (1991). [CrossRef]
  9. F. Meyer, “Un algorithme optimal de partage des eaux,” in Proceedings 8e 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, M. Gabbouj, “Fast watershed algorithms: analysis and extensions,” in Nonlinear Image Processing V, E. R. Dougherty, J. T. Astola, H. G. Longbotham, eds., Proc. SPIE2180, 209–220 (1994). [CrossRef]
  11. R. Arrathoon, “Historical perspectives: optical crossbars and optical computing,” in Digital Optical Computing, R. Arrathoon, ed., Proc. SPIE752, 2–11 (1987). [CrossRef]
  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, O. R. Mitchell, “Threshold decomposition of gray-scale morphology into binary morphology,” IEEE Trans. Pattern Anal. Mach. Intel. 11(1), 31–42 (1989). [CrossRef]
  14. L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE Trans. Image Process. 2(2), 176–201 (1993). [CrossRef]
  15. N. Michael, R. Arrathoon, “Optoelectronic pipeline architecture for morphological image processing,” Appl. Opt. 36(8), 1718–1725 (1997).
  16. S. Beucher, M. Bilodeau, “Road segmentation and obstacle detection by a fast watershed transformation,” in Proceedings of the Intelligent Vehicles 1994 Symposium (Institute of Electricaland 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, J. C. Serra, eds., Proc. SPIE1769, 212–226 (1992).
  18. N. Rougon, F. Preteux, “Quantitative automated analysis of cornea endothelial cell images,” in Image Algebra and Morphological Image Processing V, E. R. Dougherty, P. D. Gader, M. Schmitt, eds., Proc. SPIE2300, 133–144 (1994). [CrossRef]
  19. N. Michael, R. Arrathoon, “Optical PLA implementation for selected complex morphological algorithms,” in Hybrid Image and Signal Processing V, D. P. Casasent, A. G. Tescher, eds., Proc. SPIE2751, 186–198 (1996). [CrossRef]
  20. D. N. Levin, X. Hu, K. K. Tan, “Surface of the three-dimension MR images created with volume rendering,” Radiology 171, 277–280 (1989). [PubMed]
  21. K. H. Hoehne, M. Bomans, A. Pommert, M. Reimer, U. Tiede, G. Wiebecke, “Rendering tomographic volume data: adequacy of methods for different modalities and organs,” in 3D Imaging in Medicine: Algorithms, Systems, Applications, K. H. Hoehne, H. Fuchs, S. M. Pizer, eds. (Springer-Verlag, Berlin, 1990), pp. 197–215. [CrossRef]
  22. J. C. Bezdek, L. O. Hall, 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, M. S. Silbiger, “MRI segmentation: methodsand applications,” Magn. Resonance Imaging 13(3), 343–368 (1995). [CrossRef]
  24. M. C. Clark, L. O. Hall, D. B. Goldgof, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, “MRI segmentation using fuzzy clustering techniques,” IEEE Eng. Medicine Biol. 13(5), 730–742 (1994). [CrossRef]
  25. W. Connor, 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, E. R. Dougherty, eds., Proc. SPIE1568, 327–334 (1991).
  26. M. E. Brummer, R. M. Mersereau, R. L. Eisner, R. J. Lewine, “Automatic detection of brain contours in MRI data sets,” IEEE Trans. Medical Imaging 12(2), 153–166 (1993). [CrossRef]
  27. R. Acharya, Y. Ma, “Segmentation algorithm for cranial magnetic resonance images,” in Medical Imaging VI: Image Processing, M. H. Loew, ed., Proc. SPIE1652, 50–61 (1992).
  28. M. Bomans, K. H. Hoehne, U. Tiede, M. Riemer, “3-D segmentation of MR images of the head for 3-D display,” IEEE Trans. Medical Imaging 9, 177–183 (1990). [CrossRef]
  29. KHOROS is an image processing system developed by Khoral, Inc., Albequerque, NM.
  30. J. Barrera, G. J. F. Banon, R. Lotufo, “Mathematical morphology toolbox for the Khoros system,” in Image Algebra and Morphological Image Processing V, E. R. Dougherty, P. D. Gader, M. Schmitt, eds., Proc. SPIE2300, 241–252 (1994). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited