A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Segmentation-based multilayer diagnosis lossless medical image compression
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
| Hi-index | 0.00 |
The authors introduce a near-lossless scheme for the compression of digital mammogram databases. In the scheme a self-organizing neural network is first used to separate the breast area from the background. Then an optimized JPEG coding algorithm is introduced to code the segmented breast area only. The combined segmentation/compression procedure is motivated by the massive storage requirement of mammograms. The proposed scheme exploits the fact that a large proportion of the mammogram consists of uninteresting background, and the breast region occupies only a small area. The experimental results have confirmed that the scheme is capable of extending beyond the compression limits of conventional transform coding methods and achieving a far lower bit rate. As a result, the current approach provides an efficient means for the storage and transmission of digital mammogram databases.