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27 April 1995 Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis
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Abstract
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.
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Jianhua Xuan, Tulay Adali, Yue Joseph Wang, and Richard M. Steinman "Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis", Proc. SPIE 2431, Medical Imaging 1995: Image Display, (27 April 1995); https://doi.org/10.1117/12.207620
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