The impact of image information on compressibility and degradation in medical image compression

The aim of the study was to demonstrate and critically discuss the influence of image information on compressibility and image degradation. The influence of image information on image compression was demonstrated on the axial computed tomography images of a head. The standard Joint Photographic Expert Group (JPEG) and JPEG 2000 compression methods were used in compression ratio (CR) and in quality factor (QF) compression modes. Image information was estimated by calculating image entropy, while the effects of image compression were evaluated quantitatively, by file size reduction and by local and global mean square error (MSE), and qualitatively, by visual perception of distortion in high and low contrast test patterns. In QF compression mode, a strong correlation between image entropy and file size was found for JPEG (r=0.87, p < 0.001) and JPEG 2000 (r=0.84, p < 0.001), while corresponding local MSE was constant (4.54) or nearly constant (2.36-2.37), respectively. For JPEG 2000 CR compression mode, CR was nearly constant (1:25), while local MSE varied considerably (2.26 and 10.09). The obtained qualitative and quantitative results clearly demonstrate that image degradation highly depends on image information, which indicates that the degree of image degradation cannot be guaranteed in CR but only in QF compression mode. CR is therefore not a measure of choice for expressing the degree of image degradation in medical image compression. Moreover, even when using QF compression modes, objective evaluation, and comparison of the compression methods within and between studies is often not possible due to the lack of standardization of compression quality scales.     

Quality of Compressed Medical Images

Previous studies have shown that Joint Photographic Experts Group (JPEG) 2000 compression is better than JPEG at higher compression ratio levels. However, some findings revealed that this is not valid at lower levels. In this study, the qualities of compressed medical images in these ratio areas (∼20), including computed radiography, computed tomography head and body, mammographic, and magnetic resonance T1 and T2 images, were estimated using both a pixel-based (peak signal to noise ratio) and two 8 × 8 window-based [Q index and Moran peak ratio (MPR)] metrics. To diminish the effects of blocking artifacts from JPEG, jump windows were used in both window-based metrics. Comparing the image quality indices between jump and sliding windows, the results showed that blocking artifacts were produced from JPEG compression, even at low compression ratios. However, even after the blocking artifacts were omitted in JPEG compressed images, JPEG2000 outperformed JPEG at low compression levels. We found in this study that the image contrast and the average gray level play important roles in image compression and quality evaluation. There were drawbacks in all metrics that we used. In the future, the image gray level and contrast effect should be considered in developing new objective metrics.     

Effect of image compression on telepathology. A randomized clinical trial

CONTEXT: For practitioners deploying store-and-forward telepathology systems, optimization methods such as image compression need to be studied. OBJECTIVE: To determine if Joint Photographic Expert Group (JPG or JPEG) compression, a glossy image compression algorithm, negatively affects the accuracy of diagnosis in telepathology. DESIGN: Double-blind, randomized, controlled trial. SETTING: University-based pathology departments. PARTICIPANTS: Resident and staff pathologists at the University of Illinois, Chicago, and University of Cincinnati, Cincinnati, Ohio. INTERVENTION: Compression of raw images using the JPEG algorithm. MAIN OUTCOME MEASURES: Image acceptability, accuracy of diagnosis, confidence level of pathologist, image quality. RESULTS: There was no statistically significant difference in the diagnostic accuracy between noncompressed (bit map) and compressed (JPG) images. There were also no differences in the acceptability, confidence level, and perception of image quality. Additionally, rater experience did not significantly correlate with degree of accuracy. CONCLUSIONS: For providers practicing telepathology, JPG image compression does not negatively affect the accuracy and confidence level of diagnosis. The acceptability and quality of images were also not affected.     

Evaluation of Irreversible Compression Ratios for Medical Images Thin Slice CT and Update of Canadian Association of Radiologists (CAR) Guidelines

In June 2008, the Canadian Association of Radiologists published its Standards for Irreversible Compression in Digital Diagnostic Imaging within Radiology (Canadian Association of Radiologists 2012). The study suggested that at low levels of compression there was no difference in diagnostic accuracy between uncompressed JPEG and JPEG 2000. There were two exceptions; CT neurological and CT body images resulted in lower rating of image quality (Koff et al., J Digit Imaging 22(6):569–78, 2009). The slice thicknesses used in the previous study were greater than 5 mm. However, other studies (Ringl et al., Radiology 240:869–87, 2006) suggest that thin CT slices might modify image tolerance to irreversible compression. Therefore, a new clinical evaluation using CT slices less than 3 mm was initiated. We examined CT images in four body regions (chest, body, musculoskeletal, and neurological). Twenty-five radiologists from across Canada participated. Each read a total of 70 CTs in his specialty; 10 at each of seven levels of compression (uncompressed, JPEG and JPEG 2000 at low, medium, and high compression (varying by region)). Each reader diagnosed the case, rated his confidence, and compared the compressed to the uncompressed image and rated the degree of degradation. Data were analyzed for sensitivity, specificity, accuracy, confidence, and degradation at three levels and two types of compression as well as the original image. There were no overall differences in sensitivity, specificity, accuracy, or confidence. JPEG images, at all levels of compression, were rated lower in terms of perceived difference (4.16/5 vs. 4.53/5 for JPEG 2000 and 4.68/5 for uncompressed). However, the rating of perceived difference was not significantly correlated with accuracy. Analysis of individual body regions did not reveal any systematic effects of compression in any region.     

Evaluation of Irreversible JPEG Compression for A Clinical Ultrasound Practice

A prior ultrasound study indicated that images with low to moderate levels of JPEG and wavelet compression were acceptable for diagnostic purposes. The purpose of this study is to validate this prior finding using the Joint Photographic Experts Group (JPEG) baseline compression algorithm, at a compression ratio of approximately 10:1, on a sufficiently large number of grayscale and color ultrasound images to attain a statistically significant result. The practical goal of this study is to determine if it is feasible for radiologists to use irreversibly compressed images as an integral part of the day to day ultrasound practice (ie, perform primary diagnosis with, and store irreversibly compressed images in the ultrasound PACS archive). In this study, 5 Radiologists were asked to review 300 grayscale and color static ultrasound images selected from 4 major anatomic groups. Each image was compressed and decompressed using the JPEG baseline compression algorithm at a fixed quality factor resulting in an average compression ratio of approximately 9:1. The images were presented in pairs (original and compressed) in a blinded fashion on a PACS workstation in the ultrasound reading areas, and radiologists were asked to pick which image they preferred in terms of diagnostic utility and their degree of certainty (on a scale from 1 to 4). Of the 1499 total readings, 50.17% (95% confidence intervals at 47.6%, and 52.7%) indicated a preference for the original image in the pair, and 49.83% (95% confidence intervals at 47.3%, and 52.0%) indicated a preference for the compressed image. These findings led the authors to conclude that static color and gray-scale ultrasound images compressed with JPEG at approximately 9:1 are statistically indistinguishable from the originals for primary diagnostic purposes. Based on the authors laboratory experience with compression and the results of this and other prior studies, JPEG compression is now being applied to all ultrasound images in the authors' radiology practice before reading. No image quality-related issues have been encountered after 12 months of operation (approximately 48000 examinations).     

基于ISA-DWT的MR图像压缩新算法

本研究提出一种新的ISA-DWT（整数到整数的形状自适应离散小波变换）的核磁共振（MR）图像压缩算法,对变换后的系数采用适合于形状自适应离散小波变换的修改的SPIHT算法进行编码,并增加上下文自适应算术编码以提高其压缩性能.本研究算法可将前景区域和背景区域生成的压缩码流完全分离,且小波域的系数个数和图像域的个数相同.实验结果表明,对于MR图像,本研究算法的压缩性能明显优于JPEG2000中的最大位移算法.     

A Mobile Tele-Radiology Imaging System with JPEG2000 for an Emergency Care

The aim of this study was to design a tele-radiology imaging system for rapid emergency care via mobile networks and to assess the diagnostic feasibility of the Joint Photographic Experts Group 2000 (JPEG2000) radiological imaging using portable devices. Rapid patient information and image exchange is helpful to make clinical decisions. We assessed the usefulness of the mobile tele-radiology system by measuring both a quantitative method, PNSR calculation, for image qualities, and its transmission time via mobile networks in different mobile networks, respectively; code division multiple access evolution-data optimized, wireless broadband, and high-speed downlink packet access; and the feasibility of the JPEG2000 computed tomography (CT) images by qualitatively assessing with the Alberta stroke program early CT score method with 12 CT image cases (seven normal and five abnormal cases). We found that the quality of the JPEG2000 radiological images was satisfied quantitatively and was judged as acceptable qualitatively at 5:1 and 10:1 compression levels for the mobile tele-radiology imaging system. The JPEG2000-format radiological images achieved a fast transmission while maintaining a diagnosis quality on a portable device via mobile networks. Unfortunately, a PDA device, having a limited screen resolution, posed difficulties in reviewing the JPEG2000 images regardless of the compression levels. An ultra mobile PC was preferable to study the medical image. The mobile tele-radiology imaging systems supporting JPEG2000 image transmission can be applied to actual emergency care services under mobile computing environments.     

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。     

医用内窥镜图像自适应量化压缩编化码方法研究

本文提出了一种与ＪＰＥＧ标准完全兼容的医用内窥镜图像自适应量化压缩编码方法,方法采用二次扫描的措施,根据原始图像的频谱分布特点,自适应地修正ＪＰＥＧ推荐的量化表。实验结果表明：该方法较之于标准ＪＰＥＧ图像压缩,峰值信噪比（ＰＳＮＲ）明显提高,可在相同压缩比下,保持更多的图像细节,特别适合于医学图像的压缩。     

A comparison of wavelet and Joint Photographic Experts Group lossy compression methods applied to medical images

This presentation focuses on the quantitative comparison of three lossy compression methods applied to a variety of 12-bit medical images. One Joint Photographic Exports Group (JPEG) and two wavelet algorithms were used on a population of 60 images. The medical images were obtained in Digital Imaging and Communications in Medicine (DICOM) file format and ranged in matrix size from 256 × 256 (magnetic resonance [MR]) to 2,560 × 2,048 (computed radiography [CR], digital radiography [DR], etc). The algorithms were applied to each image at multiple levels of compression such that comparable compressed file sizes were obtained at each level. Each compressed image was then decompressed and quantitative analysis was performed to compare each compressed-thendecompressed image with its corresponding original image. The statistical measures computed were sum of absolute differences, sum of squared differences, and peak signal-to-noise ratio (PSNR). Our results verify other research studies which show that wavelet compression yields better compression quality at constant compressed file sizes compared with JPEG. The DICOM standard does not yet include wavelet as a recognized lossy compression standard. For implementers and users to adopt wavelet technology as part of their image management and communication installations, there has to be significant differences in quality and compressibility compared with JPEG to justify expensive software licenses and the introduction of proprietary elements in the standard. Our study shows that different wavelet implementations vary in their capacity to differentiate themselves from the old, established lossy JPEG.     

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。     

JPEG2000 for automated quantification of immunohistochemically stained cell nuclei: a comparative study with standard JPEG format

The Joint Photographic Experts Group (JPEG) standard format is one of the most widely used in image compression technologies. More recently, JPEG2000 format has emerged as a state-of-the-art technology that provides substantial improvements in picture quality at higher compression ratios. However, there has been no attempt to date to determine which of the two compression formats produces less variability in the automated evaluation of immunohistochemically stained digital images in agreement with their compression rates and complexity degrees. The evaluation of Ki67 and FOXP3 immunohistochemical nuclear markers was performed in a total of 329 digital images: 47 were captured in uncompressed Tagged Image File Format (TIFF), 141 were converted to three JPEG compressed formats (47 each with 1:3, 1:23 and 1:46 compression) and 141 were converted to three JPEG2000 compressed formats (47 each with 1:3, 1:23 and 1:46 compression). The count differences between images in TIFF versus JPEG formats were compared with those obtained between images in TIFF versus JPEG2000 formats at the three levels of compression. It was found that, using JPEG2000 compression, the results of the stained nuclei count are close enough to the results obtained with uncompressed images, especially in highly complex images at minimum and medium compression. Otherwise, in images of low complexity, JPEG and JPEG2000 had similar count efficiency to that of the original TIFF images at all compression levels. These data suggest that JPEG2000 could give rise to an efficient means of storage, reducing file size and storage capacity, without compromise on the immunohistochemical analytical quality.     

A method for splitting digital value in radiological image compression

A new decomposition method using image splitting and gray-level remapping has been proposed for image compression, particularly for images with high contrast resolution. The effects of this method are especially evident in this radiological image compression study. In these experiments, the impact of this decomposition method was tested on image compression by employing it with two coding techniques on a set of clinically used CT images and several laser film digitized chest radiographs. One of the compression techniques used as zonal full-frame bit-allocation in the discrete cosine transform (DCT) domain, which is an enhanced full-frame DCT technique that has been proven to be an effective technique for radiological image compression. The other compression technique used was vector quantization with pruned tree-structured encoding, which through recent research has also been found to produce a low mean-square error and a high compression ratio. The parameters used in this study were mean-square error and the bit rate required for the compressed file. In addition to these parameters, the differences between the original and reconstructed images were presented so that the specific artifacts generated by both techniques could be discerned through visual perception.     

JPEG2000有损压缩对核医学图像的压缩效果的客观定量研究

从线源图像获得线源扩展函数、调制转移函数和峰-总计数比等参数,定量研究JPEG 2000有损压缩对核医学图像的压缩效果,并与实际分辨率图像和模拟分辨率图像的压缩效果进行对比。实验发现,随压缩比的增加,峰-总计数比曲线逐步下降,且噪声水平越高,曲线下降越快;但调制转移函数曲线和线源扩展函数曲线的半高宽无明显变化;随压缩比的增加,分辨率图像上可辨认出的条纹逐渐减少。实验结果表明,在压缩比和噪声水平变化时,峰-总计数比的变化与图像质量的变化规律一致,是用于图像压缩效果评价的较好的客观定量参数。     

Direct digital lateral cephalometry: the effects of JPEG compression on image quality

OBJECTIVES: This study used an aluminium test object to assess the effect of the Joint Photographics Expert Group (JPEG) compression algorithm, on direct digital cephalometric image quality. METHODS: The aluminium block of 15 steps, with 20 holes in each step was radiographed in a Planmeca Proline 2002 digital cephalometric machine with Dimaxis2 software. Six different JPEG compression ratios were used to capture the cephalometric images. These ratios were 60%, 70%, 80%, 90%, TOP QUALITY JPEG (TQJPEG 98%) and TIFF (uncompressed). The images were taken at 68 kV and 12 mA with a 7 s exposure. Six experienced observers viewed the monitor displayed images, which were presented randomly. This was repeated one month later. The number of holes detected by each observer was plotted against each compression ratio. Intra-observer and inter-observer reproducibility was calculated using the Mann-Whitney U-test. Differences between the compression ratios were assessed using a Kruskal-Wallis one-way analysis of variance. RESULTS: When comparing intra-observer reproducibility, it was found that there were only four of 36 comparisons that showed a statistically significant difference (Observer 1: 60% (P=0.004), TQJPEG (P=0.019); Observer 2: TIFF (P=0.005); Observer 3: 90% (P=0.007)). Statistically, there was no significant difference with inter-observer reproducibility. There was no statistically significant difference between the image quality obtained from each compression ratio. CONCLUSIONS: The results showed that JPEG compression does not have any effect on the perceptibility of landmarks in the aluminium test object used in this study.     

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。     

Clinical Evaluation of Compression Ratios using JPEG2000 on Computed Radiography Chest Images

The efficient compression of radiographic images is of importance for improved storage and network utilization in support of picture archiving and communication systems (PACS) applications. The DICOM Working Group 4 adopted JPEG2000 as an additional compression standard in Supplement 61 over the existing JPEG. The wavelet-based JPEG2000 can achieve higher compression ratios with less distortion than the Discrete Cosine Transform (DCT)-based JPEG algorithm. However, the degradation of JPEG2000-compressed computed radiography (CR) chest images has not been tested comprehensively clinically. The authors evaluated the diagnostic quality of JPEG2000-compressed CR chest images with compression ratios from 5:1 to 200:1. An ROC (receiver operating characteristic analysis) and t test were performed to ascertain clinical performance using the JPEG2000-compressed images. The authors found that compression ratios as high as 20:1 can be utilized without affecting lesion detectability. Significant differences between the original and the compressed CR images were not recognized up to compression ratio of 50:1 within a confidence level of 99%.     

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.     

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.     

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.