1 May 1994 Edge-preserving image compression for magnetic-resonance images using dynamic associative neural networks (DANN)-based neural networks
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Abstract
With the tremendous growth in imaging applications and the development of filmless radiology, the need for compression techniques that can achieve high compression ratios with user specified distortion rates becomes necessary. Boundaries and edges in the tissue structures are vital for detection of lesions and tumors, which in turn requires the preservation of edges in the image. The proposed edge preserving image compressor (EPIC) combines lossless compression of edges with neural network compression techniques based on dynamic associative neural networks (DANN), to provide high compression ratios with user specified distortion rates in an adaptive compression system well-suited to parallel implementations. Improvements to DANN-based training through the use of a variance classifier for controlling a bank of neural networks speed convergence and allow the use of higher compression ratios for `simple' patterns. The adaptation and generalization capabilities inherent in EPIC also facilitate progressive transmission of images through varying the number of quantization levels used to represent compressed patterns. Average compression ratios of 7.51:1 with an averaged average mean squared error of 0.0147 were achieved.
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Tat Chee Wan, Mansur R. Kabuka, "Edge-preserving image compression for magnetic-resonance images using dynamic associative neural networks (DANN)-based neural networks", Proc. SPIE 2164, Medical Imaging 1994: Image Capture, Formatting, and Display, (1 May 1994); doi: 10.1117/12.174035; https://doi.org/10.1117/12.174035
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