Abstract
Biomedical High-Throughput Screening (HTS) requires specific properties of image compression. While especially when archiving a huge number of images of one particular experiment the time factor is often rather secondary, other features like lossless compression and high compression ratio are much more important. Due to the similarity of all images within one experiment series, a content based compression seems to be especially applicable. Biologically inspired techniques, particularly Artificial Neural Networks (ANN) are an interesting and innovative tool for adaptive intelligent image compression, although with JPEG2000 a promising alternative has become available.
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References
Janzen, W.P.: High Throughput Screening: Methods and Protocols. Humana Press, Totowa (2002)
Fisher, Y.: Fractal Image Compression. Theory and Application, vol. 2. Springer, Telos (1996)
Cottrell, G., Munro, P., Zipser, D.: Image compression by Backpropagation: An example of extensional programming. In: Sharkey, N. (ed.) Models of Cognition: A Review of Cognition Science. Intellect., Norwood, NJ, pp. 297–311 (1990)
Carrato, S.: Neural networks for image compression. In: Gelenbe, E. (ed.) Neural Networks: Advances and Applications 2, pp. 177–198. Elsevier North Holland, Amsterdam (1992)
Wang, L., Oja, E.: Image compression by MLP and PCA neural networks. In: Eighth Scandinavian Conference on Image Analysis, pp. 1317–1324 (1993)
Amerijckx, C., Verleysen, M., Thissen, P., Legat, J.D.: Image compression by Self-Organized Kohonen Map. IEEE Transactions on Neural Networks 9, 503–507 (1998)
Murray, J.D., Vanryper, W., Russell, D.: Encyclopedia of Graphics File Formats. O’Reilly, UK (1996)
Miano, J.: Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP. Benjamin Cummings / Addison Wesley, San Francisco, Ca (2002)
Wayner, P.: Compression Algorithms for Real Programmers. Morgan Kaufmann, San Francisco (1999)
Pennebaker, W.B., Mitchell, J.L.: JPEG: Still Image Data Compression Standard. Kluwer International, Dordrecht (1992)
Taubman, D.S., Marcellin, M.W.: JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer International, Dordrecht (2000)
Holst, G.C.: CCD Arrays, Cameras and Displays, 2nd edn., Chicago, Il. Encyclopaedia Britannica (1998)
Pallas-Areny, R., Webster, J.G.: Sensors and Signal Conditioning, 2nd edn. Wiley, Hoboken (2000)
Minsky, M., Papert, S.: Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge (1969)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362. MIT Press, Cambridge (1986)
Lippmann, R.P.: An introduction to computing with neural nets. IEEE ASSP Magazine 4, 4–23 (1987)
Seiffert, U.: Growing Multi-Dimensional Self-Organizing Maps for Motion Detection. In: Self-Organizing Neural Networks: Recent Advances and Applications, pp. 95–120. Springer, Heidelberg (2001)
Earnshaw, G.: Image complexity measure. Technical Report CT92-0015, Centre for Intelligent Systems, University of Plymouth (1995)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, London (2001)
Seiffert, U., Jain, L. (eds.): Self-Organizing Neural Networks: Recent Advances and Applications. Studies in Fuzziness and Soft Computing, vol. 78. Springer, Heidelberg (2001)
Carpenter, G.A., Grossberg, S.: The ART of adaptive pattern recognition by a self-organizing neural network. IEEE Computer 21, 77–88 (1988)
Bartfai, G.: Hierarchical clustering with ART neural networks. In: Proceedings of the IEEE 1994 International Conference on Neural Networks, vol. 2, pp. 940–944. IEEE Press, Los Alamitos (1994)
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Seiffert, U. (2004). Biologically Inspired Image Compression in Biomedical High-Throughput Screening. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds) Biologically Inspired Approaches to Advanced Information Technology. BioADIT 2004. Lecture Notes in Computer Science, vol 3141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27835-1_31
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DOI: https://doi.org/10.1007/978-3-540-27835-1_31
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