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Vector quantizer of medical image using wavelet transform and enhanced neural network

机译:使用小波变换和增强的神经网络的医学图象传染媒介量化器

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Vector quantizer takes care of special image features like edges also and hence belongs to the class of quantizers known as second generation coders. This paper proposes a vector quantization using wavelet transform and enhanced SOM algorithm for medical image compression. We propose the enhanced self-organizing algorithm to improve the defects of SOM algorithm, which, at first, reflects the error between the winner node and the input vector to the weight adaptation by using the frequency of the winner node. Secondly, it adjusts the weight in proportion to the present weight change and the previous weight change as well. To reduce the blocking effect and Improve the resolution, we construct vectors by using wavelet transform and apply the enhanced SOM algorithm to them. Our experimental results show that the proposed method energizes the compression ratio and decompression ratio.
机译:向量量化器也照顾特殊图像,如边缘,因此属于称为第二代编码器的量化器。本文提出了使用小波变换和增强型SOM算法的矢量量化进行医学图像压缩。我们提出了增强的自组织算法来提高SOM算法的缺陷,首先,通过使用Winner节点的频率将获胜者节点和输入向量之间的误差反射到权重自适应。其次,它与本权重变化的比例成比例地调节重量,并且同样的重量变化也是如此。为了减少阻塞效果并提高分辨率,我们通过使用小波变换构造向量并将增强的SOM算法应用于它们。我们的实验结果表明,该方法的含量激励压缩比和减压比。

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