医学图像无损压缩与有损压缩技术的进展

本文对医学图像无损压缩和有损压缩的概念和应用进行了分析比较 ,并简要介绍了几种近年来发展的图像无损压缩方法 ,重点介绍了有损压缩中的小波图像压缩技术和分形图像压缩技术。在医学图像的压缩中 ,通过有效地结合无损压缩和有损压缩技术 ,可以在得到医学要求的图像保真度的前提上 ,达到较高的压缩比     

医学图像无损压缩与有损压缩技术的进展

本文对医学图像无损压缩和有损压缩的概念和应用进行了分析比较，并简要介绍了几种近年来发展的图像无损压力方法，重点介绍了有损压缩中的小波图像压缩技术和分形图像压缩技术。在医学图像的压缩中，通过有效地结合无损压缩和有损压缩技术，可以在得到医学要求的图像保真度的前提上，达到较高的压缩比。     

生物医学信号的近无损压缩

生物医学信号的压缩在临床中具有广泛的应用，但在实际应用中，医生们普遍认为有损压缩技术会使诊断信息丢失，只有无损压缩才能不失真地保留所有信息，因而临床诊断必须使用无损压缩。然而，事实并非如此，首先，在信号的采集和数字化的过程中就会引入量化误差和其他各种误差，即使用无损压缩，实际也是有损的；其次，无损压缩的压缩比很低，其压缩性能远不能与有损压缩相比，所以，在医学领域真正具有广泛应用需求的还是有损压缩技术，关键是如何控制好误差以确保信号的可信度。而一般的有损压缩技术，由于其对信号失真度缺乏严格的控制，因而不适合应用于医学领域，所以有必要研究特殊的有损压缩技术，这就是近无损压缩技术。为此，我们提出了一个能有效表征信号可信度的指标，在此基础上，结合生物医学信号非线性、非平稳性的特点，设计了一个基于上下文的生物医学信号近无损压缩算法，实验结果表明近无损压缩在保证信号可信度的前提下获得了比无损压缩更好的压缩结果。最后，我们还就近无损压缩技术的研究方向作了有益的探讨。     

基于BW算法的高采样率心电数据无损压缩

目前对心电数据压缩的研究主要集中在对低采样率心电数据的压缩,我们提出了一种基于BW(Burrows-Wheeler)算法对高采样率心电数据的无损压缩算法.首先对原始心电数据进行差分变换,将部分16位二进制差值表示为8位,然后对差分结果进行前移编码,使得相同字符集中于某一段区域,最后通过算术编码得到高压缩比.结果表明,该算法不仅适用于高采样率体表心电数据的压缩,而且也适用于心内心电数据的压缩, 平均压缩比分别达到3.547和3.608.同现有的心电无损压缩算法相比,它在压缩效果上获得了较大改进.另外针对高采样率心电数据,使用该算法进行无损压缩也可以得到较好的压缩效果.     

基于提升小波的医学数据压缩算法及其DSP实现

远程医疗系统日益成熟,但数据量的急剧增长给数据的压缩和传输带来了一系列问题.DSP器件的发展使得各种复杂算法的嵌入式系统移植成为可能.参考JPEG2000标准,以提升小波变换为基础,以嵌入式零树小波编码(EZW)为核心进行医学数据压缩.针对提升小波原址运算的特点,采用多种汇编级代码优化策略,将算法移植到DSP系统中,对心电图及CT图像实现了一种支持渐进传输的快速压缩算法.实验结果表明:与Mallat算法相比,该算法能够节省DSP的数据空间和运算时间,从而使嵌入式系统得以高效运行.     

一个生物医学信号的无损压缩算法

当前生物医学领带压缩技术主要偏重于有损压缩技术的研究，较少研究无损压缩技术，然后由于在临床和科研应用中首先要求的是生物医学信号必须具有高可信度，而一般的有损压缩技术很难满意其需要，因此特别期望使用无损的压缩技术，本文给出了一个低复杂性的基于上下文的生物医学信号无损压缩算法，它在确保信号不失真的前提下，给出了较高的压缩比。     

医学图像压缩的现状及发展方向

本文概述医学图像压缩的现状，包括了有损压缩与无损压缩两个方面。我们比较集中地讨论了有损压缩的方法，并就其中如何达到“无视觉损失”与“信息保持”一致，从而能较好地解决与医学图像压缩突出相关的法律责任问题提出了一个有研究意义的新方向。     

医学图像压缩的现状及发方向

本文概述医学图像压缩的现状，包括了有损压缩与无损压缩两个方面。我们比较集中中了有损压缩的方法，并就其中如何达到“无视觉损失”与“信息保持”一致，从而能较好地解决与医学压缩突出相关的法律现任问题提出一个有研究意义的新方向。     

嵌入式零树小波编码在医学图像压缩中的应用

目的:对嵌入式零树小波编码算法提出改进,实现CT图像压缩。方法:分析嵌入式零树小波编码的原理和算法并探讨小波基的选择,针对嵌入式零树小波编码算法的缺点进行改进,研究设计实现改进算法的程序,进行CT图像压缩。结果:在相同压缩比的情况下,改进的嵌入式零树小波编码算法获得的峰值信噪比比原有算法获得的峰值信噪比高,特别是在高压缩比的情况下,图像的主观质量和峰值信噪比都有了较大的改善。结论:改进的嵌入式零树小波编码算法比原有EZW算法更加优越,能够满足医学图像的传输和诊断要求。     

联合图像专家组2000图像压缩方法的核医学应用研究

为研究联合图像专家组2000(Jo in t Photograph ic Expert G roup 2000,JPEG 2000)图像压缩方法在核医学中的应用,将无病变和有病变核医学静态图像用JPEG 2000软件压缩。对无损压缩图像,测量其压缩比。对有损压缩图像,由医生阅片,根据其结论作接收器操作特性(R ece iver operating characteristic,ROC)分析,获得各种图像压缩比的ROC曲线下的面积(A rea under curve,AUC),以其大小评价图像诊断质量;并将原始图像组AUC与各有损压缩图像组的AUC作配对t检验。实验发现,无损压缩的图像压缩比为(1.34±0.05)∶1。而有损压缩比越大,AUC越小。原始图像与压缩图像比较,压缩比为10∶1时没有显著性差异,压缩比更大时则有显著性差异。实验结果表明,无损压缩方法压缩比低,实用意义不大。有损压缩比不大于10∶1时,核医学静态图像的诊断质量得以保留。对核医学中的其它图像形式,可根据的图像性质,特别是固有统计噪声的大小,适当增减压缩比。     

利用DCT分量差值压缩ECG数据的方法

由于心脏活动的有序性和各心电活动周期波形的相似性,各心电活动周期波形的DCT（离散时间余弦变换）分量也具有一定的相似性。根据这一特点,本文提出了在首先使用DCT压缩心电图（ECG）数据的基础上,进一步利用各ECG周期的DCT分量的差值来压缩数据的方法。     

基于小波包最优基的心电图压缩

给出一种新的心电图压缩方法，该方法是在小波库中通以信息熵作为代数价函数寻找最优基，实现心电图压缩。仿真结果显示，该方法压缩比大，信息损失小，能够较好的恢复原有的信号。     

A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error.

This paper describes a hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression. First, the ECG signal is wavelet transformed using four different discrete wavelet transforms (Daubechies, Coiflet, Biorthogonal and Symmlet). All the wavelet transforms are based on dyadic scales and decompose the ECG signals into five detailed levels and one approximation. Then, the wavelet coefficients are linearly predicted, where the error corresponding to the difference between these coefficients and the predicted ones is minimized in order to get the best predictor. In particular, the residuals of the wavelet coefficients are uncorrelated and hence can be represented with fewer bits compared to the original signal. To further increase the compression rate, the residual sequence obtained after linear prediction is coded using a newly developed coding technique. As a result, a compression ratio (Cr) of 20 to 1 is achieved with percentage root-mean square difference (PRD) less than 4%. The algorithm is compared to an alternative compression algorithm based on the direct use of wavelet transforms. Experiments on selected records from the MIT-BIH arrhythmia database reveal that the proposed method is significantly more efficient in compression. The proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels.     

一种基于复合编码的心电数据压缩算法

本文提出了一种复合心电数据压缩方法，该算法根据ECG数据的特征变化，提取出每路ECG的心搏模板，从而把信号分成三部分；心搏模板，残差，位置参数，在保证恢复信号低失真的情况下，先对残余误差进行LADT编码，再利用Huffman的无损压缩方法进行全部数据二次压缩，与其它压缩方法相比，在同样的信息损伤下，该算法可获得更高的数据压缩比，本文提出的方法，也可应用到图像数据和其它数据的压缩中。     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

利用DCT分量差值压缩ECG数据的方法

由于心脏活动的有序性和各心电活动周期波形的相似性 ,各心电活动周期波形的DCT(离散时间余弦变换 )分量也具有一定的相似性。根据这一特点 ,本文提出了在首先使用DCT压缩心电图 (ECG)数据的基础上 ,进一步利用各ECG周期的DCT分量的差值压缩数据的方法     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

On lossy transform compression of ECG signals with reference to deformation of their parameter values

Electrocardiogram (ECG) signals are the most prominent biomedical signal type used in clinical medicine. Their compression is important and widely researched in the medical informatics community. In the previous literature compression efficacy has been investigated only in the context of how much known or developed methods reduced the storage required by compressed forms of original ECG signals. Sometimes statistical signal evaluations based on, for example, root mean square error were studied. In previous research we developed a refined method for signal compression and tested it jointly with several known techniques for other biomedical signals. Our method of so-called successive approximation quantization used with wavelets was one of the most successful in those tests. In this paper, we studied to what extent these lossy compression methods altered values of medical parameters (medical information) computed from signals. Since the methods are lossy, some information is lost due to the compression when a high enough compression ratio is reached. We found that ECG signals sampled at 400 Hz could be compressed to one fourth of their original storage space, but the values of their medical parameters changed less than 5% due to compression, which indicates reliable results.     

基于小波变换的心电信号准无损压缩算法

提出了基于小波变换的心电信号准无损压缩算法。在对原始信号进行一级小波分解的基础上，根据高频分量和低频分量所占位数的不同分别进行无损压缩。实验结果表明该方法失真度非常小，而且算法简单，运算速度快。     

Compression of ECG signals by optimized quantization of discrete cosine transform coefficients.

This paper presents an ECG compressor based on optimized quantization of Discrete Cosine Transform (DCT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each DCT block is quantized using a quantization vector and a threshold vector that are specifically defined for each signal. These vectors are defined, via Lagrange multipliers, so that the estimated entropy is minimized for a given distortion in the reconstructed signal. The optimization method presented in this paper is an adaptation for ECG of a technique previously used for image compression. In the last step of the compressor here proposed, the quantized coefficients are coded by an arithmetic coder. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. To assess the performance of the proposed compressor, 2-minute sections of all 96 records of the MIT-BIH Arrhythmia Database were compressed at different PRD values, and the corresponding compression ratios were computed. We also present traces of test signals before and after the compression/decompression process. The results show that the proposed method achieves good compression ratios (CR) with excellent reconstruction quality. An average CR of 9.3:1 is achieved for PRD equal to 2.5%. Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors.