一种基于随机回归辨识理论的医学图像无损压缩方法

详细地介绍了一种基于随机回归识理论的医学图像无埚压缩方法，该方法的性能通过十幅Ｘ线胸部图像与ＤＰＣＭ方法进行了比较，实验结果表明：这种方法对实现医学图像的无损压缩是非常有效的。     

An efficient compression scheme for 4-D medical images using hierarchical vector quantization and motion compensation

This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-D motion estimation to create a homogenous preprocessed data to be compressed by a 3-D image compression algorithm using hierarchical vector quantization. A new block distortion measure, called variance of residual (VOR), and three 3-D fast block matching algorithms are used to improve the motion estimation process in term of speed and data fidelity. The 3-D image compression process involves the application of two different encoding techniques based on the homogeneity of input data. Our method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods producing similar compression ratios. The combination of 3-D motion estimation using VOR and hierarchical vector quantization contributes to the good performance.     

改进的小波域医学图像序列的运动估计

医学图像序列压缩是远程医疗系统中的重要技术，而运动估计在视频序列压缩中起着关键作用。我们提出了一种改进的正方形-菱形搜索算法来实现医学图像序列的运动估计。这种改进的正方形-菱形算法减少了搜索点数。我们将其应用于小波域的医学图像序列的运动估计，并对数字减影血管造影图像序列（DSA）进行实验。结果表明，改进后的小波域正方形-菱形算法较其他算法精度高。     

基于二维DCT的医学图象压缩及FPGA实现

本文介绍了一种适用于JPEG医学图象压缩的二维DCT的FPGA设计方案。该设计充分利用了可编程逻辑器件的灵活性以及器件本身所带的硬件资源，如嵌入式乘法器。二维离散余弦变换采用了行列分解的方法，并通过快速算法在很大程度上减少了硬件实现的复杂度，提高了模块的吞吐量，并且具有实时，高精度的优点。     

Adaptive threshold-based block classification in medical image compression for teleradiology

Telemedicine, among other things, involves storage and transmission of medical images, popularly known as teleradiology. Due to constraints on bandwidth and storage capacity, a medical image may be needed to be compressed before transmission/storage. Among various compression techniques, transform-based techniques that convert an image in spatial domain into the data in spectral domain are very effective. Discrete cosine transform (DCT) is possibly the most popular transform used in compression of images in standards like Joint Photographic Experts Group (JPEG). In DCT-based compression the image is split into smaller blocks for computational simplicity. The blocks are classified on the basis of information content to maximize compression ratio without sacrificing diagnostic information. The present paper presents a technique along with computational algorithm for classification of blocks on the basis of an adaptive threshold value of variance. The adaptive approach makes the classification technique applicable across the board to all medical images. Its efficacy is demonstrated by applying it to CT, X-ray and ultrasound images and by comparing the results against the JPEG in terms of various objective quality indices.     

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.     

一种基于人类感知的医学图象压缩方法

作者详细地介绍了一种基于人类感知的医学图象压缩算法，它利用人类视觉的运动特性，空间频率特性及时间频率特性对静止灰度图象进行有限失真压缩，该方法能大大压缩图象数据提高压缩比，对医学图象的压缩是一种比较有效的方法。     

Adaptive compression algorithm from projections: Application on medical greyscale images

Image compression plays a crucial role in medical imaging, allowing efficient manipulation, storage, and transmission of binary, grey-scale, or colour images. Nevertheless, in medical applications the need to conserve the diagnostic validity of the image requires the use of lossless compression methods, producing low compression factors. In this paper, a novel near-lossless compression algorithm from projections, which almost eliminates both redundant information and noise from a greyscale image while retaining all relevant structures and producing high compression factors, is proposed. The algorithm is tested on experimental medical greyscale images from different modalities and different body districts and results are reported.     

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。     

Optimization of block size for DCT-based medical image compression

In view of the increasing importance of medical imaging in healthcare and the large amount of image data to be transmitted/stored, the need for development of an efficient medical image compression method, which would preserve the critical diagnostic information at higher compression, is growing. Discrete cosine transform (DCT) is a popular transform used in many practical image/video compression systems because of its high compression performance and good computational efficiency. As the computational burden of full frame DCT would be heavy, the image is usually divided into non-overlapping sub-images, or blocks, for processing. This paper aims to identify the optimum size of the block, in reference to compression of CT, ultrasound and X-ray images. Three conflicting requirements are considered, namely processing time, compression ratio and the quality of the reconstructed image. The quantitative comparison of various block sizes has been carried out on the basis of benefit-to-cost ratio (BCR) and reconstruction quality score (RQS). Experimental results are presented that verify the optimality of the 16 x 16 block size.     

Optimization of block size for DCT-based medical image compression

In view of the increasing importance of medical imaging in healthcare and the large amount of image data to be transmitted/stored, the need for development of an efficient medical image compression method, which would preserve the critical diagnostic information at higher compression, is growing. Discrete cosine transform (DCT) is a popular transform used in many practical image/video compression systems because of its high compression performance and good computational efficiency. As the computational burden of full frame DCT would be heavy, the image is usually divided into non-overlapping sub-images, or blocks, for processing. This paper aims to identify the optimum size of the block, in reference to compression of CT, ultrasound and X-ray images. Three conflicting requirements are considered, namely processing time, compression ratio and the quality of the reconstructed image. The quantitative comparison of various block sizes has been carried out on the basis of benefit-to-cost ratio (BCR) and reconstruction quality score (RQS). Experimental results are presented that verify the optimality of the 16 × 16 block size.     

基于存储服务的JPEG2000医学DICOM图像压缩的研究

由于医学图像所包含的数据量巨大,不利于存储和在现有网络上有效地传输,本研究使用VC++实现基于联合图像专家组2000(Joint Photographic Experts Group2000,JPEG2000)图像压缩算法对医学图像进行处理,根据医学数字图像通讯(Digital Imaging and Communicationin Medicine,DICOM)标准中存储服务的规则实现医学图像的存储服务。研究结果表明,使用JPEG2000压缩算法处理后的医学图像,不仅能大大节约存储空间,而且能降低传输时所需要的带宽。因此,JPEG2000压缩算法在DICOM推广及远程医疗的发展中具有积极意义。     

JPEG2000 Still Image Coding Quality

This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r > 0.98, p < 0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.     

基于边缘特征点匹配的临床X线自动全景成像研究

目的：本文提出了一种基于边缘特征点匹配的X线自动全景成像方法。方法：一,采用双正交小波变换与Canny算子相结合的算法实现X线图像的有效边缘提取,并通过矩阵计算得到特征点;二,利用Pearson相关系数和随机抽样一致算法来建立图像之间的相关性和匹配性;三,利用渐入渐出的融合算法实现X线无缝全景成像。结果：该算法可快速高质量地实现骨科临床无外部特征的X线图像序列全景拼接。结论：该算法对弱对比度的X线图像序列全景拼接具有很强的鲁棒性,允许X线图像序列之间存在较小的图像旋转和缩放差异,有助于减少X线使用剂量,具有较强的应用价值。     

Lossy JPEG Compression in Quantitative Angiography: the Role of X-ray Quantum Noise

In medical imaging, contrary to applications in the consumer market, the use of irreversible or lossy compression is still in its beginnings. This is due to the suspected risk of compromising the diagnostic content. Many studies have been performed, but it was not until 2008 that national activities in different countries resulted in recommendations for the safe use of irreversible image compression in clinical practice. Quantitative coronary angiography (QCA), however, poses a special problem, since here a large variation in published maximum compression factors has strengthened the general concerns about the use of lossy techniques. Up to now, the reason for the variation has not been thoroughly investigated. Reasons for the discrepancies in published compression factors are determined in this study. Since JPEG compression reduces the quantum noise of the X-ray images, the impact of compression is overestimated when interpreting any change in local diameter as an error. By taking into consideration the quantitative effect of quantum noise in QCA, it is shown that the influence of JPEG compression can be neglected for compression factors up to ten at clinically applicable X-ray doses. This limit is comparable to that found by visual analysis for aesthetic image quality. Future studies on image compression effects should take the interaction with quantum noise explicitly into consideration.     

快速显示三维医学体数据的光线投射方法

本文提出了一种快速显示三维医学体数据的光线投射方法，方法原型是基于以图像空间为序的体显示算法，但在物体空间实施加速方法。在本文中，首先我们简要介绍了图像空间为序的休绘制算法原理及步骤。接着，叙述了如何利用物体空间的包围体方法来减少追踪光线数量。最后，给出了该算法用于显示同一医学体数据内多器官的研究结果。     

整型提升小波变换的块编码在医学图像压缩中的应用

提出一种基于整型提升小波变换的图像块压缩编码方法.整型提升小波变换具有计算快速、能实现任意图像尺寸的小波算法、能在当前位置完成小波变换、节省内存等特点,而且该提升算法能同时对图像进行有损或无损压缩,因而更适应于远程医疗系统和医学图像压缩系统.基于图像块压缩编码方法不仅可以实现比特率控制,还可以实现SNR(信噪比)可缩放性,支持图像的渐进传输.     

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。     

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.     

医学图像压缩技术研究进展

随着医学成像方式的不断增加,目前迫切需要解决庞大的图像数据的存储和传输的问题,压缩成为解决该问题不可或缺的重要方法之一.对目前医学图像压缩领域的技术进行了总结和分析,详细介绍了基于感兴趣区域的压缩、无损压缩、小波变换和基于神经网络的压缩方法,并对压缩算法的评估给出了几点标准,最后对医学图像压缩领域的发展前景提出了一些看法.