Backpropagation Neural Network Implementation for Medical Image Compression
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 consi...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/453098 |
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| _version_ | 1832550963236306944 |
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| author | Kamil Dimililer |
| author_facet | Kamil Dimililer |
| author_sort | Kamil Dimililer |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3953845fe9024460a3a64c831247112e |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-3953845fe9024460a3a64c831247112e2025-02-03T06:05:22ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/453098453098Backpropagation Neural Network Implementation for Medical Image CompressionKamil Dimililer0Electrical and Electronic Engineering Department, Near East University, Nicosia, North Cyprus, Mersin 10, TurkeyMedical 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.http://dx.doi.org/10.1155/2013/453098 |
| spellingShingle | Kamil Dimililer Backpropagation Neural Network Implementation for Medical Image Compression Journal of Applied Mathematics |
| title | Backpropagation Neural Network Implementation for Medical Image Compression |
| title_full | Backpropagation Neural Network Implementation for Medical Image Compression |
| title_fullStr | Backpropagation Neural Network Implementation for Medical Image Compression |
| title_full_unstemmed | Backpropagation Neural Network Implementation for Medical Image Compression |
| title_short | Backpropagation Neural Network Implementation for Medical Image Compression |
| title_sort | backpropagation neural network implementation for medical image compression |
| url | http://dx.doi.org/10.1155/2013/453098 |
| work_keys_str_mv | AT kamildimililer backpropagationneuralnetworkimplementationformedicalimagecompression |