Application of the Neural Networks Based on Multi-valued Neurons to Classification of the Images of Gene Expression Patterns

Multi-valued neurons (MVN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN. The MVN-based neural networks are applied to temporal classification of images of gene expression patterns, obtained by confocal scanning microscopy. The classification results confirmed the efficiency of this method for image recognition. It was shown that frequency domain of the representation of images is highly effective for their description.

[1]  J. Reinitz,et al.  Rapid preparation of a panel of polyclonal antibodies to Drosophila segmentation proteins , 1998, Development Genes and Evolution.

[2]  J. Overall,et al.  Applied multivariate analysis , 1983 .

[3]  Yukio Kosugi,et al.  An image storage system using complex-valued associative memories , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Joos Vandewalle,et al.  Multi-Valued and Universal Binary Neurons , 2000 .

[5]  S. J. Press,et al.  Applied Multivariate Analysis. , 1973 .

[6]  Naum N. Aizenberg,et al.  Multi-Valued Neurons: Learning, Networks, Application to Image Recognition and Extrapolation of Temporal Series , 1995, IWANN.

[7]  Joos Vandewalle,et al.  Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications , 2012 .

[8]  Igor N. Aizenberg Neural Networks Based on Multi-valued and Universal Binary Neurons: Theory, Application to Image Processing and Recognition , 1999, Fuzzy Days.

[9]  Igor N. Aizenberg,et al.  Processing of noisy and small-detailed gray-scale images using cellular neural networks , 1997, J. Electronic Imaging.

[10]  K. R. Rao,et al.  Orthogonal Transforms for Digital Signal Processing , 1979, IEEE Transactions on Systems, Man and Cybernetics.

[11]  Shigeru Akamatsu,et al.  Invariant neural-network based face detection with orthogonal Fourier-Mellin moments , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  John Reinitz,et al.  Registration of the expression patterns of Drosophila segmentation genes by two independent methods , 2001, Bioinform..

[13]  Naum N. Aizenberg,et al.  Image recognition on the neural network based on multi-valued neurons , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[14]  D Kosman,et al.  Automated assay of gene expression at cellular resolution. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[15]  J. Todd Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[16]  Naum N. Aizenberg,et al.  Pattern Recognition Using Neural Based on Multi-valued Neurons , 1999, IWANN.

[17]  Naum N. Aizenberg,et al.  Neural Network Based on Multi-valued Neurons: Application in Image Recognition, Type of Blur and Blur Parameters Identification , 2001, IWANN.

[18]  Cornelius T. Leondes Image processing and pattern recognition , 1998 .

[19]  Naum N. Aizenberg,et al.  Application of the neural networks based on multivalued neurons in image processing and recognition , 1998, Electronic Imaging.

[20]  Jacek M. Zurada,et al.  Complex-valued multistate neural associative memory , 1996, IEEE Trans. Neural Networks.

[21]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[22]  Naum N. Aizenberg,et al.  CNN based on multi-valued neuron as a model of associative memory for grey scale images , 1992, CNNA '92 Proceedings Second International Workshop on Cellular Neural Networks and Their Applications.

[23]  A.V. Oppenheim,et al.  The importance of phase in signals , 1980, Proceedings of the IEEE.

[24]  Naum N. Aizenberg,et al.  Multi-valued and universal binary neurons: mathematical model, learning, networks, application to image processing and pattern recognition , 1996, Proceedings of 13th International Conference on Pattern Recognition.