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.     

基于SD2PCM的数字减影图象序列无损压缩算法

DPCM编码在无损压缩中已被广泛应用 ,本文充分利用DSA图象序列在减影图象 (一阶差分 )之间仍然存在一定的相关性条件下 ,进一步推广了DPCM编码方式到高阶形式 ,提出了一种基于减影差异图象的DPCM(SD2 PCM )的编码方式 ,有效地去除了图象在空间以及时间上的冗余度。理论与实验的证明都证明了 ,SD2 PCM的压缩效率要优于目前的 3D DPCM。     

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.     

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.     

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。     

基于离散小波变换的JPEG 2000标准对肺部CT图像的无损压缩

探索基于小波变换对肺部CT图像进行无损雎缩的新方法.采用基于离散小波变换方法的JPEG2000标准对3019张肺部CT图像进行无损压缩,并针对压缩后图像效果进行统计分析.结果表明,该方法不但可以达到12.0的高压缩比,而且具有较高的图像尤损压缩质量,为临床CT医学影像的储存与诊断,提供了有益的技术与方法.     

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.     

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.     

Selective medical image compression techniques for telemedical and archiving applications

Selective Image Compression (SeLIC) is a compression technique where explicitly defined regions of interest (RoI) are compressed in a lossless way whereas image regions containing unimportant information are compressed in a lossy manner. Such techniques are of great interest in telemedicine or medical imaging applications with large storage requirements. In this paper we introduce and compare techniques with different functionalities. Moreover, we investigate the impact of using wavelet transforms and JPEG as underlying lossy compression algorithm.     

Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.     

基于DCT系数的二级矢量量化压缩ECG数据的方法

本研究提出了一种新的心电信号压缩方法，该方法对心电数据进行离散余弦变换（DCT）并对DCT变换的结果进行二级矢量量化。该方法不但继承了矢量量化高压缩比的特点，而且在很大程度上降低了矢量量化所需的码书长度进而也降低了码字搜索的运算复杂度。实验证明，该算法是一种有效可行的心电信号压缩方法。     

DWT-DCT hybrid scheme for medical image compression

With the development of communication technology the applications and services of health telemetics are growing. In view of the increasingly important role played by digital medical imaging in modern health care, it is necessary for large amount of image data to be economically stored and/or transmitted. There is a need for the development of image compression systems that combine high compression ratio with preservation of critical information. During the past decade wavelets have been a significant development in the field of image compression. In this paper, a hybrid scheme using both discrete wavelet transform (DWT) and discrete cosine transform (DCT) for medical image compression is presented. DCT is applied to the DWT details, which generally have zero mean and small variance, thereby achieving better compression than obtained from either technique alone. The results of the hybrid scheme are compared with JPEG and set partitioning in hierarchical trees (SPIHT) coder and it is found that the performance of the proposed scheme is better.     

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     

DWT–DCT hybrid scheme for medical image compression

With the development of communication technology the applications and services of health telemetics are growing. In view of the increasingly important role played by digital medical imaging in modern health care, it is necessary for large amount of image data to be economically stored and/or transmitted. There is a need for the development of image compression systems that combine high compression ratio with preservation of critical information. During the past decade wavelets have been a significant development in the field of image compression. In this paper, a hybrid scheme using both discrete wavelet transform (DWT) and discrete cosine transform (DCT) for medical image compression is presented. DCT is applied to the DWT details, which generally have zero mean and small variance, thereby achieving better compression than obtained from either technique alone. The results of the hybrid scheme are compared with JPEG and set partitioning in hierarchical trees (SPIHT) coder and it is found that the performance of the proposed scheme is better.     

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively.

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Lossless Compression of Volumetric Medical Images with Improved Three-Dimensional SPIHT Algorithm

This article presents a lossless compression of volumetric medical images with the improved three-dimensional (3-D) set partitioning in hierarchical tree (SPIHT) algorithm that searches on asymmetric trees. The tree structure links wavelet coefficients produced by 3-D reversible integer wavelet transforms. Experiments show that the lossless compression with the improved 3-D SPIHT gives improvement about 42% on average over two-dimensional techniques and is superior to those of prior results of 3-D techniques. In addition, we can easily apply different numbers of decomposition between the transaxial and axial dimensions, which is a desirable function when the coding unit of a group of slices is limited in size.     

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.     

一种实用的X射线灰度图像的压缩算法

以医用Ｘ射线灰度图像为例,介绍基于象素Ｒ,Ｇ,Ｂ值的无损压缩算法在Ｘ射线图像分析系统中的应用。通过对Ｘ射线灰度图像特征、象素的Ｒ,Ｇ,Ｂ值,压缩等各个环节的设计与描述,完整地给出了该处 实现过程。     

A variance-based measure of inter-rater agreement in medical databases

The increasing use of encoded medical data requires flexible tools for data quality assessment. Existing methods are not always adequate, and this paper proposes a new metric for inter-rater agreement of aggregated diagnostic data. The metric, which is applicable in prospective as well as retrospective coding studies, quantifies the variability in the coding scheme, and the variation can be differentiated in categories and in coders. Five alternative definitions were compared in a set of simulated coding situations and in the context of mortality statistics. Two of them were more effective, and the choice between them must be made according to the situation. The metric is more powerful for larger numbers of coded cases, and Type I errors are frequent when coding situations include different numbers of cases. We also show that it is difficult to interpret the meaning of variation when the structures of the compared coding schemes differ.     

Displaying radiologic images on personal computers: Image storage and compression: Part 1

This is the third article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Part 1 of this article discusses image storage and reviews the basic concepts of information theory and image compression; part 2 will discuss specific methods of image compression. There are a wide variety of removable storage devices available to users who need to archive radiologic images on their personal computers. Tape drives have potentially very large storage capacity but slow performance. Removable SyQuest (SyQuest Technology, Femont, CA) and Bernoulli disks have near hard disk performance and can store from 100 to 150 Mbytes. Magneto-optical drives can store nearly 1 Gb on a 5.25″ disk, with somewhat slower performance. Selecting the most appropriate storage solution requires a careful balance of the user's requirements, including performance, storage needs, cost and compatibility with other users. Despite the advances in low cost high capacity storage technology, image compression remains a crucial technology for modern diagnostic radiology because digital images require such large amounts of storage. Image compression is possible because radiologic images have relatively low entropy (high information content) compared with random noise. Image compression is classified as lossless (nondestructive) or lossy (destructive). Lossless image compression commonly achieve compression ratios of 1.5:1 to 3:1 (33% to 67%), whereas lossy compression can compresses images from 3:1 to 30:1 (67% to 97%). Many lossless compression methods are enhanced by first creating a difference image using discrete pulse code modulation. All compression methods are adversely affected by image noise.