Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis
| Autor: | Tulay Adali, Yue Joseph Wang, Jianhua Xuan, Richard M. Steinman |
|---|---|
| Rok vydání: | 1995 |
| Předmět: |
Linde–Buzo–Gray algorithm
Learning vector quantization business.industry Computer science Fractal transform ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Vector quantization Image segmentation Perceptron Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence business Quantization (image processing) Image compression |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| Popis: | We present a predictive learning tree-structured vector quantization technique for medical image compression. A multi-layer perceptron (MLP) based vector predictor is employed to remove first as well as higher order correlations that exist among neighboring pixels. We use a learning tree-structured vector quantization (LTSVQ) scheme, which is based on competitive learning (CL) algorithm, to encode the residual vector. LTSVQ algorithm is computationally very efficient, easy to implement and provides performance comparable to that of LBG (Linde, Buzo and Gray) algorithm. We use computerized image analysis (image segmentation) as well as mean square error (MSE) and signal-to-noise ratio (SNR) to evaluate the quality of the compressed images. We apply the neural network based predictive LTSVQ to mammographic and magnetic resonance (MR) images, and evaluate the quality of images with different compression ratios.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. |
| Databáze: | OpenAIRE |
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