Image compression for medical diagnosis using neural networks

Authors

  • Laura Cristina Lanzarini III-LIDI (Institute of Research in Computer Sciences LIDI), Facultad de Informática. Universidad Nacional de La Plata. La Plata, 1900, Argentina.
  • María Teresa Vargas Camacho Dep. of Computer Sciences, Faculty of Exact Sciences, UNLP, Argentina
  • Amado Flores Badrán Faculty of Medical Sciences, UNLP, Argentina
  • Armando Eduardo De Giusti III-LIDI (Institute of Research in Computer Sciences LIDI), Facultad de Informática. Universidad Nacional de La Plata. La Plata, 1900, Argentina.

Keywords:

Artificial Intelligence, Neural Networks, Images Processing, Medical Diagnosis

Abstract

Images compression is a widely studied topic. Conventional situations offer variable compression ratios depending on the image in question and, in general, do not yield good results for images that are rich in tones. This work is an application of images compression of patient s computed tomographies using neural networks, which allows to carry out both compression and decompression of the images with a fixed ratio of 8:1 and a loss of 2%.

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References

[1] "Digital Image Processing". Gregory A. Baxes. De. Wiley.1994
[2] "Digital Image Processing". Rafael Gon lez. Addison Wesley.1992
[3] "Fundamentals of Digital Image Processing". Anil Jain. Prentice Hall. 1989
[4] “Graphics File Formats”. Kay & Levine.1992
[5] “Neural Networks and Fuzzy Systems”. Bart Kosko. Prentice Hall. 1992
[6] “Signal and Image Processing with Neural Networks”. Timothy Masters.Wiley & Sons. 1994
[7] “Adaptative Pattern Recognition and Neural Networks” Yoh-Han Pao.Addisson Wesley. 1989
[8] “Neural Networks and Fuzzy Logic”. Rao y Rao. MIS Press. 1995
[9] Ecuaciones Diferenciales. Shepley Ross. Editorial Reverte. 1989

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Published

2000-03-01

How to Cite

Lanzarini, L. C., Vargas Camacho, M. T., Flores Badrán, A., & De Giusti, A. E. (2000). Image compression for medical diagnosis using neural networks. Journal of Computer Science and Technology, 1(02), 7 p. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/1017

Issue

Section

Original Articles