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.     

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.     

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。     

磁共振图像中非均匀场的校正

缓慢变化的非均匀场使磁共振图像的局部统计特性发生变化 ,不同生理组织的亮度交叠分布 ,使磁共振图像的分割比其他医学图像分割困难的多。磁共振图像中的非均匀场是磁共振图像自动分割的主要障碍。人们提出了众多的磁共振图像非均匀场的校正方法 ,其中有传统的图像处理方法 ,如图像模糊、平滑、滤波 ,也有新的方法 ,如基于分割的方法 ,基于直方图的方法等。本文对这些方法进行了综述和讨论     

Fractal Analysis of Periapical Bone from Lossy Compressed Radiographs: A Comparison of Two Lossy Compression Methods

The aim of the study was to evaluate the effect of two lossy image compression methods on fractal dimension (FD) calculation. Ten periapical images of the posterior teeth with no restorations or previous root canal therapy were obtained using storage phosphor plates and were saved in TIF format. Then, all images were compressed with lossy JPEG and JPEG2000 compression methods at five compression levels, i.e., 90, 70, 50, 30, and 10. Compressed file sizes from all images and compression ratios were calculated. On each image, two regions of interest (ROIs) containing healthy trabecular bone in the posterior periapical area were selected. The FD of each ROI on the original and compressed images was calculated using differential box counting method. Both image compression and analysis were performed by a public domain software. Altogether, the FD of 220 ROIs was calculated. FDs were compared using ANOVA and Dunnett tests. The FD decreased gradually with compression level. A statistically significant decrease of the FD values was found for JPEG 10, JPEG2000 10, and JPEG2000 30 compression levels (p < 0.05). At comparable file sizes, the JPEG induced a smaller FD difference. In conclusion, lossy compressed images with appropriate compression level may be used for FD calculation.     

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     

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.     

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。     

基于Retinex理论的眼底彩色图像增强算法

眼底彩色图像存在亮度低、对比度差、局部细节丢失等问题,分析已有Retinex图像增强方法存在的问题,在此基础上提出一种改进的基于Retinex理论的眼底彩色图像增强方法。首先提取亮度分量,对亮度通道进行多尺度Retinex增强,改进将图像映射到显示器上的gain/offset 算法以及颜色恢复方法,最后对具有亮度信息的红色通道进行恢复。为验证方法的有效性,以DIARETDB0眼底图像数据库为研究对象,并与多尺度Retinex(MSR)、带色彩恢复的多尺度Retinex(MSRCR)、直方图均衡化(HE)、对比度受限自适应直方图均衡化(CLAHE) 4种经典增强算法进行比较。结果表明,所处理的图像在色彩保护、血管对比度的提升以及图像细节的增强方面比其他图像增强方法有更好的效果,信息熵提高5%～7%,峰值信噪比(PSNR)比传统方法提高1～2倍,客观评价指标明显优于当前常用的眼底图像增强方法,对进一步眼底图像的识别具有重要的意义。     

A Technique for Simulating the Effect of Dose Reduction on Image Quality in Digital Chest Radiography

Purpose: The purpose of this study is to provide a pragmatic tool for studying the relationship between dose and image quality in clinical chest images. To achieve this, we developed a technique for simulating the effect of dose reduction on image quality of digital chest images. Materials and Methods: The technique was developed for a digital charge-coupled-device (CCD) chest unit with slot-scan acquisition. Raw pixel values were scaled to a lower dose level, and a random number representing noise to each specific pixel value was added. After adding noise, raw images were post processed in the standard way. Validation was performed by comparing pixel standard deviation, as a measure of noise, in simulated images with images acquired at actual lower doses. To achieve this, a uniform test object and an anthropomorphic phantom were used. Additionally, noise power spectra of simulated and actual images were compared. Also, detectability of simulated lesions was investigated using a model observer. Results: The mean difference in noise values between simulated and real lower-dose phantom images was smaller than 5% for relevant clinical settings. Noise power spectra appeared to be comparable on average but simulated images showed slightly higher noise levels for higher spatial frequencies and slightly lower noise levels for lower spatial frequencies. Comparable detection performance was shown in simulated and actual images with slightly worse detectability for simulated lower dose images. Conclusion: We have developed and validated a method for simulating dose reduction. Our method seems an acceptable pragmatic tool for studying the relationship between dose and image quality.     

Cost-effective handling of digital medical images in the telemedicine environment

BACKGROUND: This paper concentrates on strategies for less costly handling of medical images. Aspects of digitization using conventional digital cameras, lossy compression with good diagnostic quality, and visualization through less costly monitors are discussed. METHOD: For digitization of film-based media, subjective evaluation of the suitability of digital cameras as an alternative to the digitizer was undertaken. To save on storage, bandwidth and transmission time, the acceptable degree of compression with diagnostically no loss of important data was studied through randomized double-blind tests of the subjective image quality when compression noise was kept lower than the inherent noise. A diagnostic experiment was undertaken to evaluate normal low cost computer monitors as viable viewing displays for clinicians. RESULTS: The results show that conventional digital camera images of X-ray images were diagnostically similar to the expensive digitizer. Lossy compression, when used moderately with the imaging noise to compression noise ratio (ICR) greater than four, can bring about image improvement with better diagnostic quality than the original image. Statistical analysis shows that there is no diagnostic difference between expensive high quality monitors and conventional computer monitors. CONCLUSION: The results presented show good potential in implementing the proposed strategies to promote widespread cost-effective telemedicine and digital medical environments.     

Information Entropy Measure for Evaluation of Image Quality

This paper presents a simple and straightforward method for synthetically evaluating digital radiographic images by a single parameter in terms of transmitted information (TI). The features of our proposed method are (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image noise and resolution (blur). Two acrylic step wedges with 0–1–2–3–4–5 and 0–2–4–6–8–10 mm in thickness were used as phantoms for experiments. In the present study, three experiments were conducted. First, to investigate the relation between the value of TI and image noise, various radiation doses by changing exposure time were employed. Second, we examined the relation between the value of TI and image blurring by shifting the phantoms away from the center of the X-ray beam area toward the cathode end when imaging was performed. Third, we analyzed the combined effect of deteriorated blur and noise on the images by employing three smoothing filters. Experimental results show that the amount of TI is closely related to both image noise and image blurring. The results demonstrate the usefulness of our method for evaluation of physical image quality in medical imaging.     

Projection-Based Medical Image Compression for Telemedicine Applications

Recent years have seen great development in the field of medical imaging and telemedicine. Despite the developments in storage and communication technologies, compression of medical data remains challenging. This paper proposes an efficient medical image compression method for telemedicine. The proposed method takes advantage of Radon transform whose basis functions are effective in representing the directional information. The periodic re-ordering of the elements of Radon projections requires minimal interpolation and preserves all of the original image pixel intensities. The dimension-reducing property allows the conversion of 2D processing task to a set of simple 1D task independently on each of the projections. The resultant Radon coefficients are then encoded using set partitioning in hierarchical trees (SPIHT) encoder. Experimental results obtained on a set of medical images demonstrate that the proposed method provides competing performance compared with conventional and state-of-the art compression methods in terms of compression ratio, peak signal-to-noise ratio (PSNR), and computational time.     

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.     

A Clinical Evaluation of the Image Quality Computer Program, CoCIQ

To provide an objective way of measuring image quality, a computer program was designed that automatically analyzes the test images of a contrast-detail (CD) phantom. The program gives a quantified measurement of image quality by calculating an Image Quality Figure (IQF). The aim of this work was to evaluate the program and adjust it to clinical situations in order to find the detectable level where the program gives a reliable figure of the contrast resolution. The program was applied on a large variety of images with lumbar spine and urographic parameters, from very low to very high image qualities. It was shown that the computer program produces IQFs with small variations and there were a strong linear statistical relation between the computerized evaluation and the evaluation performed by human observers (R² = 0.98). This method offers a fast and easy way of conducting image quality evaluations.     

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.     

Radiologist evaluation of a multispectral image compression algorithm for magnetic resonance images

With the advent of teleradiology and picture archiving and communication systems (PACS), the expense and time required for image transmission and long term image archiving become important. The use of validated image compression algorithms can greatly reduce these costs. A lossy, multispectral image compression scheme at compression ratios (CR) of 25∶1 and 32∶1 was used for a set of 26 different patient MR exams. The original and compressed/decompressed (CD) image sets were evaluated in a blinded fashion by four radiologists in two phases. The main objective was to determine whether radiologic interpretation would vary between the two types of CD image sets and the corresponding originals. In general, the compression algorithm caused a slight decrease in image quality; however, the interpretation of pathology did not change between the original and CD image sets. In only one case at the maximum CR=32 did one of four radiologists change the interpretation of pathology after CD. In this study, lossy multispectral image compression of MR images at CR=25 maintained diagnostic integrity. This could play a significant role in image storage and communications.     

A novel image quality index using Moran I statistics

Measurement of image quality is very important for various applications such as image compression, restoration and enhancement. Conventional methods (e.g., mean squared error; MSE) use error summation to measure quality change pixel by pixel and do not correlate well with subjective quality measurement. This is due to the fact that human eyes extract structural information from the viewing field. In this study a new quality index using a Moran I statistics is proposed. The Moran statistic that measures the sharpness from a local area is a good index of quality as most image processing techniques alter the smoothness of the image. Preliminary results show that the new quality index outperforms the MSE significantly under various types of image distortions.     

The effect of lossy discrete cosine transform compression on subtle bone fractures

Extensive research efforts have been devoted to the feasibility of picture archiving and communication systems (PACS) in recent years. The advantages of PACS are numerous but mainly include reduced cost and improvement in the operational efficiency of a PACS-based radiology department. In digital radiography, images are viewed either in hard-copy or soft-copy format. Usually, these images are subsequently compressed and archived for future evaluation. There are various methods used in image compression. In this study, computed radiography images showing subtle pediatric bone fractures were compressed with the lossy method of image compression after they had been initially evaluated on workstation monitors. These studies were subsequently evaluated by observers, who were unaware of the interpretations of these images before compression, to determine if they could detect similar abnormalities. Our conclusion is that there is no difference in the interpretation of soft-copy computed radiographic images before or after lossy 10∶1 compression in studies of subtle pediatric bone fractures. This is a US government work. There are no restrictions on its use.     

Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

In a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.