基于提升格式整数小波变换的超声图像压缩算法

本文提出了一种基于提升格式整数小波变换和改进的SPIHT编码(多级树集合分裂算法)的医学超声图像压缩算法.在压缩对象选择和小波变换方面充分考虑了超声扫描线图像的分辨率特性.与基于Mallat小波变换的标准SPIHT编码算法相比,本文算法在压缩比和重建图像峰值信噪比至少不降低的情况上,运算时间不到前者的40％,内存消耗也大大减小,因而更适合于实时图像压缩.     

小波变换在医学图像压缩中的应用

目的：探讨利用小波变换进行医学图像压缩的方法。方法：通过小波变换对图像进行时频局部化分析，将图像分解到多个尺度上。进行多分辨分析。然后对变换后的子图像的小波系数特点进行了分析，讨论了其适用于图像压缩编码的特性和优势。嵌入式零树小波图像编码算法是一种有效的图像压缩方法。在分析嵌入式零树小波图像编码算法的基础上，针对传统嵌入零树小波编码方法存在的不足之处，提出了一种改进的零树小波编码算法。结果：在获得较大压缩比的同时能保证医学图像的重建质量，可以较好地满足PACS对医学图像存储和传输的要求。结论：仿真实验表明，本方法是一种有效的医学图像压缩方法。     

基于小波的医学图像渐进编码算法的研究

医学图像的渐进编码在医学图像档案的回放和医学图像的远程传输中都有非常重要的意义.作者在研究二维图像小波分解的基础上,提出了一种新的基于小波变换的图像渐进编码算法.实验表明该算法可取得较高的压缩比和运算速度.     

小波包算法及其在医学图像压缩中的应用

简要介绍了小波包变换的原理和算法，用小波与小波包对一幅图像进行了压缩试验。结果表明，小波包压缩性能较小波压缩性能好。     

一种基于医学序列图像的"有损-无损"压缩方案

针对医学序列图像的特点,提出了基于图像配准技术的差值预测方法,以减少序列图像之间的相关冗余.对于预测差值图像,采用基于可逆整数小波变换的EBCOT编码方法,实现对"非必要-必要"区域的"有损-无损"压缩.通过对实际医学图像(CT)的实验表明,该方法具有很好的压缩性能,可以有效满足现代医学图像的压缩要求.     

基于提升小波变换和SPIHT的医学图像编码算法

目的:探讨一种基于提升小波变换和多级树集合分裂算法(set partitioning in hierarchical trees,SPIHT)的医学图像编码算法。方法:针对传统小波浮点数运算,计算量大的缺点,采用提升格式小波,结合多级树集合分裂算法和算术编码,实现对医学图像的编码。结果:在获得较高压缩比的情况下,能保证医学图像的重建质量,满足医学图像数据的存储和传输的需要。结论:仿真结果表明在相同压缩比的情况下,重建图像的峰值信噪比有明显提高,获得了较好的压缩效果。     

基于最佳截断嵌入码块编码和离散小波变换的医学图像复合压缩算法

根据医学图像信息相对集中的特点,提出了一种基于最佳截断嵌入码块编码和离散小波变换的医学图像任意形状感兴趣区域复合压缩方法,通过对图像感兴趣区域和背景区采用不同的编码方式,提高了医学图像压缩比,并确保了医学图像感兴趣区域的高质量重建。实验表明:该方法在重建图像质量和压缩比方面均达到了较好的性能。     

医学超声图像低码率环境下的编码技术研究

针对医学超声视频的结构和特点,并结合小波变换压缩编码技术,实现了低比特率环境下医学超声旬序列的渐近式传输,实验结果证明这种编码方法是有效的,可行的。     

基于整型小波变换和SPIHT的医学图像压缩

医学图像数据量大,在高效压缩的同时确保其压缩后的高保真度是医学图像压缩首要考虑的因素。使用第二代整数实现的提升格式小波变换代替原来的小波变换,保证图像的可逆性和小波特性,能够实现真正的无损压缩。实验结果表明,在此基础上完成的多集集合分裂算法(SPIHT),对医学图像的压缩更加平滑,视觉效果好,压缩效果和质量较高,提高了重构图像的PSNR。     

基于小波变换统计特征的图像压缩算法的研究

图像能量的统计分布是图像压缩处理的重要依据，在研究小波子带图像统计特性的基础上，提出了一种新的基于小波子带图像统计特征和人眼视觉特性的图像量化编码算法，实验证明，该算法具有计算简单，压缩效率较高的特点。     

基于整数小波变换和嵌入式编码的ECG信号压缩

针对一般的有损压缩方法不能同时实现无损压缩的情况,实现了一类基于整数小波变换和嵌入式编码的心电数据压缩方法,不仅能够进行有损压缩,也能够实现无损压缩.并且比较了EZW、SPIHT和SPECK三种主要的嵌入式编码算法的性能优劣,为移动心电监护以实时性、信号质量和编码可伸缩性为根据选择数据压缩方法提供了参考.     

Medical image compression based on a morphological representation of wavelet coefficients.

Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run-length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal-to-noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting.     

Interslice coding for medical three-dimensional images using an adaptive mode selection technique in wavelet transform domain

In this article the authors propose a novel interslice coding algorithm especially appropriate for medical 3-dimensional (3D) images. The proposed algorithm is based on a video coding algorithm using motion estimation/compensation and transform coding. In the algorithm, warping is adopted for motion compensation. Then, by using adaptive mode selection, an MC residual image and original image are mixed up in the wavelet transform domain for improvement in coding performance. The mixed image is then compressed by the zerotree coding method. It is proven that the adaptive mode selection technique in the wavelet transform domain is very useful for medical 3D image coding. Simulation results show that the proposed scheme provides good performance, regardless of interslice distance, and is prospective for medical 3D image compression.     

Lossless Compression on MRI Images Using SWT

Medical image compression is one of the growing research fields in biomedical applications. Most medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in health-care settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods. Furthermore, providing random access as well as resolution and quality scalability to the compressed data has become of great utility. Random access refers to the ability to decode any section of the compressed image without having to decode the entire data set. The system proposes to implement a lossless codec using an entropy coder. 3D medical images are decomposed into 2D slices and subjected to 2D-stationary wavelet transform (SWT). The decimated coefficients are compressed in parallel using embedded block coding with optimized truncation of the embedded bit stream. These bit streams are decoded and reconstructed using inverse SWT. Finally, the compression ratio (CR) is evaluated to prove the efficiency of the proposal. As an enhancement, the proposed system concentrates on minimizing the computation time by introducing parallel computing on the arithmetic coding stage as it deals with multiple subslices.     

Ultrasound and angio image compression by cosine and wavelet transforms.

The investigation results for improving lossy compression techniques for ultrasound and angio images are presented. The goal was to determine where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that the wavelet transform outperforms the discrete cosine transform applied to ultrasound and angio images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterised by a content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to an algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images. A set of recommendations for acceptable compression ratio for different medical image modalities was developed. It was carried out on the base of compression study performed by the group of angiologists and cardiologists.