Irreversible Compression of Medical Images

The volume of data from medical imaging is growing at exponential rates, matching or exceeding the decline in the costs of digital data storage. While methods to reversibly compress image data do exist, current methods only achieve modest reductions in storage requirements. Irreversible compression can achieve substantially higher compression ratios without perceptible image degradation. These techniques are routinely applied in teleradiology, and often in Picture Archiving and Communications Systems. The practicing radiologist needs to understand how these compression techniques work and the nature of the degradation that occurs in order to optimize their medical practice. This paper describes the technology and artifacts commonly used in irreversible compression of medical images.     

Quality Degradation in Lossy Wavelet Image Compression

The objective of this study was to develop a method for measuring quality degradation in lossy wavelet image compression. Quality degradation is due to denoising and edge blurring effects that cause smoothness in the compressed image. The peak Moran z histogram ratio between the reconstructed and original images is used as an index for degradation after image compression. The Moran test is applied to images randomly selected from each medical modality, computerized tomography, magnetic resonance imaging, and computed radiography and compressed using the wavelet compression at various levels. The relationship between the quality degradation and compression ratio for each image modality agrees with previous reports that showed a preference for mildly compressed images. Preliminary results show that the peak Moran z histogram ratio can be used to quantify the quality degradation in lossy image compression. The potential for this method is applications for determining the optimal compression ratio (the maximized compression without seriously degrading image quality) of an image for teleradiology.     

Lossless Watermarking for Verifying the Integrity of Medical Images with Tamper Localization

Given the ease of alteration of digital data, integrity verification and tamper detection for medical images are becoming ever more important. In this paper, instead of using the conventional irreversible block-based watermarking approach to achieve tamper localization, we propose to incorporate such functionality into the region-based lossless watermarking scheme. This is achieved by partitioning an image into certain non-overlapping regions and appending the associated local authentication information directly into the watermark payload. A region of authentication, which can be flexibly specified by the user, is partitioned into small regions in a multilevel hierarchical manner. Such hierarchical structure allows the user to easily adjust the localization accuracy, and makes the tamper detection efficient. Experimental results demonstrate the effectiveness of tamper localization.     

High-resolution digital teleradiology: A perspective

Teleradiology has come a long way, from analog transmission systems using slow-scan television over standard telephone lines, to present-day, commercially available, microcomputer-based, low-resolution teleradiology systems. However, there exists a need to address the high-resolution end of the medical imaging categories, namely chest radiographs and mammograms, to firmly establish teleradiology. The availability of high-resolution image digitizers, display units, and digital hard copiers has made high-resolution digital teleradiology a feasible concept. Although the use of satellite channels can speed up the transmission of radiographic image data, with widespread acceptance of high-resolution teleradiology systems in the foreseeable future, the sheer amount of data involved in this field will give rise to problems of data transmission and storage. Data compression schemes can bring down the amount of data handled and can have a great economic impact on future teleradiology systems. We have developed a number of compression techniques for reversible compression of medical images. Our experiments have shown that lossless compression of the order of 4:1 is possible for a class of high-resolution medical images. Use of pattern recognition techniques offers the potential to bring down these data rates even further. We plan to use these techniques in a prototype high-resolution teleradiology system being developed. In this paper, we trace some of the developments in teleradiology and image data compression, and present a perspective for teleradiology in the 1990s.     

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.     

A Review of Medical Image Watermarking Requirements for Teleradiology

Teleradiology allows medical images to be transmitted over electronic networks for clinical interpretation and for improved healthcare access, delivery, and standards. Although such remote transmission of the images is raising various new and complex legal and ethical issues, including image retention and fraud, privacy, malpractice liability, etc., considerations of the security measures used in teleradiology remain unchanged. Addressing this problem naturally warrants investigations on the security measures for their relative functional limitations and for the scope of considering them further. In this paper, starting with various security and privacy standards, the security requirements of medical images as well as expected threats in teleradiology are reviewed. This will make it possible to determine the limitations of the conventional measures used against the expected threats. Furthermore, we thoroughly study the utilization of digital watermarking for teleradiology. Following the key attributes and roles of various watermarking parameters, justification for watermarking over conventional security measures is made in terms of their various objectives, properties, and requirements. We also outline the main objectives of medical image watermarking for teleradiology and provide recommendations on suitable watermarking techniques and their characterization. Finally, concluding remarks and directions for future research are presented.     

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.     

Hiding clinical information in medical images: A new high capacity and reversible data hiding technique

A new high capacity and reversible data hiding scheme for e-healthcare applications has been presented in this paper. Pixel to Block (PTB) conversion technique has been used as an effective and computationally efficient alternative to interpolation for the cover image generation to ensure reversibility of medical images. A fragile watermark and Block Checksum (computed for each 4 × 4 block) have been embedded in the cover image for facilitating tamper detection and tamper localization, and hence content authentication at receiver. The EPR, watermark data and checksum data has been embedded using Intermediate Significant Bit Substitution (ISBS) to avoid commonly used LSB removal/replacement attack. Non-linear dynamics of chaos have been put to use for encrypting the Electronic Patient Record (EPR)/clinical data and watermark data for improving the security of data embedded. The scheme has been evaluated for perceptual imperceptibility and tamper detection capability by subjecting it to various image processing and geometric attacks. Experimental results reveal that the proposed system besides being completely reversible is capable of providing high quality watermarked images for fairly high payload. Further, it has been observed that the proposed technique is able to detect and localise the tamper. A comparison of the observed results with that of some state-of-art schemes show that our scheme performs better.     

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.     

Symmetry-Based Biomedical Image Compression

Image compression techniques aim at reducing the amount of data needed to accurately represent an image, such that the image can be economically transmitted or archived. This paper deals with employing symmetry as a parameter for compression of biomedical images. The approach presented in this paper offers great potential in complete lossless compression of the biomedical image under consideration, with the reconstructed image being mathematically identical to the original image. The method comprises getting rid of the redundant data and encoding the non-redundant data for the purpose of regenerating the image at the receiver section without any observable change in the image data.     

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.     

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.     

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。     

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。     

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。     

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.     

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.     

Secure and efficient health data management through multiple watermarking on medical images

The landscape of healthcare delivery and medical data management has significantly changed over the last years, as a result of the significant advancements in information and communication technologies. Complementary and/or alternative solutions are needed to meet the new challenges, especially regarding security of the widely distributed sensitive medical information. Digital watermarking is a recently established research area with many applications; nevertheless, the potential of this technology to contribute value-added services to medical information management systems has only recently started to be realized by the research community. The paper presents a review of research efforts in the area of medical-oriented watermarking and proposes a wavelet-based multiple watermarking scheme; this scheme aims to address critical health information management issues, including origin and data authentication, protection of sensitive data, and image archiving and retrieval. In accordance with the strict limitations applying to medical images, the scheme allows the definition of a region of interest (ROI) whose diagnostic value is protected, since the only additional information embedded therein aims at integrity control. The robustness of the method is enhanced through a form of hybrid coding, which includes repetitive embedding of BCH encoded watermarks. The experimental results on different medical imaging modalities demonstrate the efficiency and transparency of the watermarking scheme.     

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.     

“Evaluation of a Very Low-Cost and Simple Teleradiology Technique”

This paper describes and analyzes a proposed solution of fundamental limitative factor of teleradiology to overcome the teleradiology usages problems in underdeveloped and developing countries. The goal is to achieve a very simple and cost-efficient way to take advantage of teleradiology in anywhere even in remote and rural areas. To meet the goal of this study, the following methodology which is consists of two main procedures was done: (1) Using a digital camera in order to provide a digital image from radiographs. (2) Using an image compression tool in order to compress digital images. The results showed that there is no significant difference between digital images (non-compress and compress images) and radiographic films. Also, there was a logic relationship between the diagnostic quality and diagnostic accuracy. Since the maximum percent of diagnostic accuracy can be seen among “Good” quality images and the minimum to was related “Poor”. The results of our study indicate that a digital camera could be utilized to capture digital images from radiographic films of chest x-ray. To reduce the size of digital images, a lossy compression technique could be applied at compression percent of 50 or less without any significant differences. The compressed images can be sent easily by email to other places for consultation and also they can be stored with a smaller size.