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。     

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。     

基于小波分析的医学图像压缩技术进展

作为图像存诸和传输系统(picture archiving & communication system,PACS)的关键技术之一,医学图像压缩算法的优劣对PACS的性能有着重要的影响,小波分析由于其多分辨率分析特性而在医学图像压缩中得到了广泛应用.从小波变换医学图像压缩、小波包变换医学图像压缩和多小波变换医学图像压缩三个方面综述了小波医学图像压缩方法及其进展,总结对比了这些方法的优点和缺陷,并针对其不足之处提出了改进方向.     

医学图像压缩算法研究及进展

高分辨率的X光片和高对比度的CT、MRI等三维医学影像具有很大的信息量,使医学影像诊断系统、PACS系统及远程医疗等数字化技术应用面临巨大的挑战,迫切需要高效实用的医学图像压缩技术.与一般图像压缩相比,医学图像压缩具有其特殊性和复杂性,其压缩必须严格保证诊断的可靠性.本文从医学图像的特殊性出发,对医学图像压缩算法进行了系统的论述和比较,并对未来的研究进行了展望.     

医学图像的零树小波编码

背景:医学数字图像必须是高质量的、高分辨率,所以数据量很大,如此巨大的数据量不利于图像存档与传输系统的运行和数字化医院、远程医疗的实现。因此,图像压缩成为图像存档与传输系统要解决的重要问题。目的:分析零树小波变编码算法原理并编程实现对医学数字图像的压缩,使之能够满足医学图像的传输和诊断要求。方法:应用嵌入式零树小波编码算法,探讨小波基和小波变换层数的选择,编程实现对医学数字图像的压缩。结果与结论:选择双正交小波基对医学图像进行4层小波变换实现压缩,获得了较高的峰值信噪比,取得了较好的压缩效果。     

基于JPEG技术的CT图像压缩方法的研究

CT图像在医学影像诊断中占有重要地位，CT图像压缩技术是PACS系统的重要组成部分。JPEG静止图像压缩标准目前已成为一种广泛应用的图像压缩技术。JPEG充分利用人眼的视觉特性，对DCT系数的低频系数进行细量化，而对高频系数进行粗量化，但未对图像的边缘特征进行特殊的处理。而图像的部分边缘特征又正是隐含于DCT高频系数中。文中从图像边缘信息的谱分析入手，通过理论推导及实例分析得到平直单边缘图像子块其DCT系数能量的分布规律，并以此为理论基础设计出JPEG中DCT系数量化表(Q表)，实验结果表明该Q表对非平直边缘图象子块没有影响，而对含有平直边缘图像子块的边缘信息进行了有效的保持。     

基于Windows平台的DICOM医学影像显示技术研究

DICOM3．0是PACS、HIS系统和远程医疗技术实现的关键所在，它规范了医学图像及各种数字信息在异构系统和设备之间存储、传送时的文件格式和语义描述。通过对标准DICOM3．0医学图像格式的分析，以Windows2000为平台，深入讨论了标准CT和MR医学图像在通用微机上的开窗显示技术，并在DICOM标准原有算法的基础上提出了一种改进的图像显示算法。     

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.     

基于小波包变换的医学图像融合方法

为满足医学图像临床辅助诊断和治疗的需要,将小波包变换和自适应算子相结合,提出一种新的医学图像融合算法.算法首先对已配准的医学图像进行小波包分解,并采用自适应算子对小波系数及分解子图像进行处理,通过小波包重建,获得高质量的医学融合图像.该方法克服了小波变换不能兼顾图像高频成分的缺陷,并且可以根据不同的医学图像自动调整融合规则的权重系数,有效避免了设置固定权重系数造成的融合误差.实例融合仿真验证了算法的有效性和先进性.     

基于Mablab7.0的小波医学图像压缩性能研究

选择合适的小波基是小波医学图像压缩的一个重要方面，因为不同的小波具有不同的特性，从而会导致不同的压缩比。为了研究小波的压缩性能，找出影响医学图像压缩比的主要因素，我们利用Matlab7．0的小波工具箱分别对三类共30幅不同的医学图像进行了压缩实验，得出了每类医学图像在不同小波下的压缩比。结果表明，Daubechies系列小波、Symmlet系列小波、Coiflet系列小波具有相对稳定的压缩比；影响医学图像压缩效果的关键因素是小波基的特性而不是医学图像的类型。     

Displaying radiologic images on personal computers: Image storage and compression—Part 2

This is part 2 of our article on image storage and compression, the third article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Image compression is classified as lossless (nondestructive) or lossy (destructive). Common lossless compression algorithms include variable-length bit codes (Huffman codes and variants), dictionary-based compression (Lempel-Ziv variants), and arithmetic coding. Huffman codes and the Lempel-Ziv-Welch (LZW) algorithm are commonly used for image compression. All of these compression methods are enhanced if the image has been transformed into a differential image based on a differential pulse-code modulation (DPCM) algorithm. The LZW compression after the DPCM image transformation performed the best on our example images, and performed almost as well as the best of the three commercial compression programs tested. Lossy compression techniques are capable of much higher data compression, but reduced image quality and compression artifacts may be noticeable. Lossy compression is comprised of three steps: transformation, quantization, and coding. Two commonly used transformation methods are the discrete cosine transformation and discrete wavelet transformation. In both methods, most of the image information is contained in a relatively few of the transformation coefficients. The quantization step reduces many of the lower order coefficients to 0, which greatly improves the efficiency of the coding (compression) step. In fractal-based image compression, image patterns are stored as equations that can be reconstructed at different levels of resolution.     

Lossless medical image compression by multilevel decomposition

Lossless image coding is important for medical image compression because any information loss or error caused by the image compression process could affect clinical diagnostic decisions. This paper proposes a lossless compression algorithm for application to medical images that have high spatial correlation. The proposed image compression algorithm uses a multilevel decomposition scheme in conjunction with prediction and classification. In this algorithm, an image is divided into four subimages by subsampling. One subimage is used as a reference to predict the other three subimages. The prediction errors of the three subimages are classified into two or three groups by the characteristics of the reference subimage, and the classified prediction errors are encoded by entropy coding with corresponding code words. These subsampling and classified entropy coding procedures are repeated on the reference subimage in each level, and the reference subimage in the last repetition is encoded by conventional differential pulse code modulation and entropy coding. To verify this proposed algorithm, it was applied to several chest radiographs and computed tomography and magnetic resonance images, and the results were compared with those from well-known lossless compression algorithms.     

基于小波的医学超声图像斑点噪声抑制方法

斑点噪声是超声图像中固有的噪声。本文提出了一种新的去除斑点噪声的方法，这种方法结合中值滤波和多尺度非线性小波软阈值的优点，首先把原网像进行对数转换，然后把对数转换后的图像进行中值滤波处理，从而把转换后的图像分成两部分，对每一部分进行小波分析，假设小波系数服从广义高斯分布（GGD），利用小波系数的统计特性估计出各个部分各个尺度的阈值，最后用软阈值方法对上述两部分分别去噪。实验结果表明，本文提出的方法在有效去除斑点噪声方面，优于中值滤波，维纳滤波和多尺度非线性阈值算法（MSSNT-A）。     

Wavelet based compression of medical ultrasound images using vector quantization

In this paper, an efficient technique for compression of medical ultrasound (US) images is proposed. The technique is based on wavelet transform of the original image combined with vector quantization (VQ) of high-energy subbands using the LBG algorithm. First, we analyse the statistical behaviour of wavelet coefficients in US images across various subbands and scales. The analysis show that most of the image energy is concentrated in one of the detail subband, either in the vertical detail subband (most of the time) or in the horizontal subband. The other two subbands at each decomposition level contribute negligibly to the total image energy. Then, by exploiting this statistical analysis, a low-complexity image coder is designed, which applies VQ only to the highest energy subband while discarding the other detail subbands at each level of decomposition. The coder is tested on a series of abdominal and uterus greyscale US images. The experimental results indicate that the proposed method clearly outperforms the JPEG2000 (Joint Photographers Expert Group) encoder both qualitatively and quantitatively. For example, without using any entropy coder, the proposed method yields a peak signal to noise ratio gain of 0.2 dB to 1.2 dB over JPEG2000 on medical US images.     

High efficient ECG compression based on reversible round-off non-recursive 1-D discrete periodized wavelet transform

Error propagation and word-length-growth are two intrinsic effects influencing the performance of wavelet-based ECG data compression methods. To overcome these influences, a non-recursive 1-D discrete periodized wavelet transform (1-D NRDPWT) and a reversible round-off linear transformation (RROLT) theorem are developed. The 1-D NRDPWT can resist truncation error propagation in decomposition processes. By suppressing the word- length-growth effect, RROLT theorem enables the 1-D NRDPWT process to obtain reversible octave coefficients with minimum dynamic range (MDR). A non-linear quantization algorithm with high compression ratio (CR) is also developed. This algorithm supplies high and low octave coefficients with small and large decimal quantization scales, respectively. Evaluation is based on the percentage root-mean-square difference (PRD) performance measure, the maximum amplitude error (MAE), and visual inspection of the reconstructed signals. By using the MIT-BIH arrhythmia database, the experimental results show that this new approach can obtain a superior compression performance, particularly in high CR situations.