Backpropagation Neural Network Implementation for Medical Image Compression-Reference-Cited by-同舟云学术

Backpropagation Neural Network Implementation for Medical Image Compression

Published:2013 Issue: Volume:2013 Page:1-8
ISSN:1110-757X
Container-title:Journal of Applied Mathematics
language:en
Short-container-title:Journal of Applied Mathematics

Author:

Dimililer Kamil¹^ORCID

Affiliation:

1. Electrical and Electronic Engineering Department, Near East University, Nicosia, North Cyprus, Mersin 10, Turkey

Abstract

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.

Publisher

Hindawi Limited

Subject

Applied Mathematics

Link

http://downloads.hindawi.com/journals/jam/2013/453098.pdf

Reference17 articles.

1. Adaptive threshold-based block classification in medical image compression for teleradiology

2. Wavelet-based color image compression: exploiting the contrast sensitivity function

3. Lossless image compression with multiscale segmentation

4. A novel approach to medical image compression

5. Robust watermarking and compression for medical images based on genetic algorithms

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