OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 37, Iss. 17 — Jun. 10, 1998
  • pp: 3628–3638

Improved classification robustness for noisy cell images represented as principal-component projections in a hybrid recognition system

Maricor Soriano and Caesar Saloma  »View Author Affiliations

Applied Optics, Vol. 37, Issue 17, pp. 3628-3638 (1998)

View Full Text Article

Enhanced HTML    Acrobat PDF (1691 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Different types of cells are recognized from their noisy images by use of a hybrid recognition system that consists of a learning principal-component analyzer and an image-classifier network. The inputs to the feed-forward backpropagation classifier are the first 15 principal components of the 10 × 10 pixel image to be classified. The classifier was trained with clear images of cells in metaphase, unburst cells, and other erroneous patterns. Experimental results show that the recognition system is robust to image scaling and rotation, as well as to image noise. Cell recognition is demonstrated for images that are corrupted with additive Gaussian noise, impulse noise, and quantization errors. We compare the performance of the hybrid recognition system with that of a conventional three-layer feed-forward backpropagation network that uses the raw image directly as input.

© 1998 Optical Society of America

OCIS Codes
(070.5010) Fourier optics and signal processing : Pattern recognition
(200.4260) Optics in computing : Neural networks

Maricor Soriano and Caesar Saloma, "Improved Classification Robustness for Noisy Cell Images Represented as Principal-Component Projections in a Hybrid Recognition System," Appl. Opt. 37, 3628-3638 (1998)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. M. Soriano and C. Saloma, “Cell classification by a learning principal components analyzer and a backpropagation neural network,” Bioimaging 3, 168–175 (1995). [CrossRef]
  2. H. Lodish, D. Baltimore, A. Berk, S. Zipursky, P. Matsudaira, and J. Darnell, Molecular Cell Biology (Scientific American, New York, 1995).
  3. B. Alberts, D. Bray, J. Lewis, M. Raff, K. Roberts, and J. Watson, Molecular Biology of the Cell (Garland, New York, 1989).
  4. R. Gonzalez and R. Woods, Digital Image Processing (Addison-Wesley, New York, 1993).
  5. T. Masters, Signal and Image Processing With Neural Networks (Wiley, New York, 1993).
  6. S. Haykin, Neural Network—A Comprehensive Foundation (Macmillan, New York, 1994).
  7. M. McCord Nelson, and W. Illington, A Practical Guide to Neural Nets (Addison-Wesley, Reading, Mass., 1991).
  8. C. Bishop, Neural Networks for Statistical Pattern Recognition (Oxford U. Press, Oxford, 1994).
  9. T. Watkin and A. Rau, “The statistical mechanics of learning a rule,” Rev. Mod. Phys. 65, 499–556 (1994). [CrossRef]
  10. A. Jain and J. Mao, “Guest editorial: special issue on artificial neural networks and statistical pattern recognition,” IEEE Trans. Neural Networks 8, 1–3 (1997). [CrossRef]
  11. J. Blue, G. Candela, P. Grother, R. Chellapa, and C. Wilson, “Evaluation of pattern classifiers for fingerprint and OCR applications,” Patt. Recog. 27, 485–501 (1994). [CrossRef]
  12. L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. Le Cun, U. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwritten digit recognition,” in Proceedings of the International Conference on Pattern Recognition (IEEE Comput. Soc. Press, Los Alamitos, Calif., 1994), Vol. 2, pp. 77–82.
  13. Y. Qi and B. Hunt, “Signature verification using global and grid features,” Patt. Recog. 27, 1621–1629 (1994). [CrossRef]
  14. D. Burton, “Text-dependent speaker verification using vector quantization source coding,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 133–140 (1987). [CrossRef]
  15. F. Tsung and G. Cottrell, “Learning in recurrent finite difference networks,” Int. J. Neural Sys. 6, 249–255 (1995). [CrossRef]
  16. G. Cottrell and J. Metcalfe “EMPATH: face, emotion and gender recognition using holons,” in Vol. 3 of Advances in Neural Information Processing Systems Series (Morgan Kaufmann, San Mateo, Calif., 1991), pp. 564–571.
  17. S. Lawrence, C. Lee Giles, A. C. Tsoi, and A. Back, “Face recognition: a convolutional neural-network approach,” IEEE Trans. Neural Networks 8, 98–113 (1997). [CrossRef]
  18. D. Beymer and T. Poggio, “Image representations for visual learning,” Science 272, 1905–1909 (1996). [CrossRef] [PubMed]
  19. R. Chellapa, C. Wilson, and S. Sirohey, “Human and machine recognition of faces,” Proc. IEEE 83, 705–740 (1995). [CrossRef]
  20. I. Cox, J. Ghosn, and P. Yianilos, “Feature-based face recognition using mixture–distance,” in Computer Vision and Pattern Recognition (IEEE Press, Piscataway, N.J., 1996).
  21. D. Rosenthal and L. Mango, “Applications of neural networks for interactive diagnosis of anatomic pathology specimens,” in Compendium on the Computerized Cytology and Histology Laboratory, Tutorials of Cytology, G. Weid, P. Bartels, D. Rosenthal, and U. Schenck, eds. (Karger, Chicago, 1994), pp. 173–184.
  22. M. Astion and P. Wilding, “The application of backpropagation neural networks to problems in pathology,” Arch. Pathol. Lab. Med. 116, 995–1001 (1992). [PubMed]
  23. S. Shiotani, T. Fukuda, and F. Arai, “Cell recognition by image processing (recognition of dead or living plant cells by neural network),” JSME Int. J. Ser. C 371, 233–240 (1994).
  24. C. An, L. Petrovic, and A. Marchevsky, “The application of image analysis and neural network technology to the study of large cell liver cells,” Hepatocell Carcin. 26, 1224–2230 (1997).
  25. M. Brickley, J. Coupe, and J. Shepherd, “Performance of a computer-simulated neural network trained to categorize normal, premalignant and malignant oral smears,” J. Oral Pathol. Med. 25, 424–430 (1996). [CrossRef] [PubMed]
  26. A. Dawson, R. Austin, and D. Weinberg, “Nuclear grading of breast carcinoma by image analysis: classification by multivariate and neural network analysis,” Am. J. Clin. Pathol. (Suppl. 4) 95, 529–530.
  27. L. Mango, “Deducing false negatives in clinical practice: the role of neural network technology,” Am. J. Obstetr. Gynecol. 175, 1114–1119 (1996). [CrossRef]
  28. J. Terrillon, “Image preprocessing for rotation-invariant pattern recognition in the presence of signal-dependent noise,” Appl. Opt. 35, 1879–1893 (1996). [CrossRef] [PubMed]
  29. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Macmillan, New York, 1990).
  30. K. Fukunaga and J. Young, “Pattern recognition and neural engineering,” in Neural Networks, Concepts, Applications and Implementations, P. Antognetti and V. Milutinovic, eds. (Prentice Hall, EngleWood Cliffs, N.J., 1991), Vol. 1, pp. 10–33.
  31. L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” J. Opt. Soc. Am. A 4, 519–524 (1987). [CrossRef] [PubMed]
  32. L. Gupta, R. Mohammed, and R. Tammana, “A neural network approach to robust shape classification,” Pattern Recog. 23, 563–568 (1990). [CrossRef]
  33. T. Sanger, “Optimal unsupervised learning in a single-layer linear feed forward neural network,” Neural Networks 2, 459–473 (1989). [CrossRef]
  34. J. Tou and R. Gonzalez, Pattern Recognition Principles (Addison-Wesley, London, 1974).
  35. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 2nd ed. (McGraw-Hill, New York, 1984).
  36. G. Parry, “Speckle patterns in partially coherent light,” in Laser Speckle and Related Phenomena, Vol. 9 of Topics in Applied Physics Series (Springer-Verlag, Berlin, 1984).
  37. C. Saloma, S. Kawata, and S. Minami, “Laser diode microscope that generates weakly speckled images,” Opt. Lett. 15, 203–205 (1990). [CrossRef] [PubMed]
  38. P. Chavel, “Optical noise and temporal coherence,” J. Opt. Soc. Am. 70, 935–1012 (1980). [CrossRef]
  39. J. Chamberlain, The Principles of Interferometric Spectroscopy (Wiley, New York, 1979), Chap. 9.
  40. V. Daria, O. Nakamura, C. Palmes-Saloma, K. Fujita, C. Saloma, H. Kondoh, and S. Kawata, “Long-depth imaging of turbid biological samples by two-photon fluorescence microscopy,” paper presented at the Nineteenth Meeting of the Japan Society for Laser Microscopy, Nagoya, Japan, 8–10 May 1997.
  41. S. Inoue, Video Microscopy (Plenum, New York, 1986).
  42. J. Proakis and D. Manolakis, Digital Signal Processing—Principles, Algorithm, and Applications, 2nd ed. (Macmillan, New York, 1992), pp. 41–43.
  43. L. Levi, Applied Optics—A Guide to Optical System Design (Wiley, New York, 1968), Vol. 1, pp. 152–154.
  44. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C—The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, New York, 1992).

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