Backpropagation Neural Network Implementation for Medical Image Compression

Author

Dimililer, Kamil

Source

Journal of Applied Mathematics

Issue

Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2013-12-31

Country of Publication

Egypt

No. of Pages

8

Main Subjects

Mathematics

Abstract EN

Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity.

An ideal image compression system must yield high-quality compressed image with high compression ratio.

In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

American Psychological Association (APA)

Dimililer, Kamil. 2013. Backpropagation Neural Network Implementation for Medical Image Compression. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-472771

Modern Language Association (MLA)

Dimililer, Kamil. Backpropagation Neural Network Implementation for Medical Image Compression. Journal of Applied Mathematics No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-472771

American Medical Association (AMA)

Dimililer, Kamil. Backpropagation Neural Network Implementation for Medical Image Compression. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-472771

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-472771