Quantitative image quality evaluation of MR images using perceptual difference models

The authors are using a perceptual difference model (Case-PDM) to quantitatively evaluate image quality of the thousands of test images which can be created when optimizing fast magnetic resonance (MR) imaging strategies and reconstruction techniques. In this validation study, they compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to Case-PDM and similar models. The authors found that Case-PDM compared very favorably to human observers in double-stimulus continuous-quality scale and functional measurement theory studies over a large range of image quality. The Case-PDM threshold for nonperceptible differences in a 2-alternative forced choice study varied with the type of image under study, but was approximately 1.1 for diffuse image effects, providing a rule of thumb. Ordering the image quality evaluation models, we found in overall Case-PDM approximately IDM (Sarnoff Corporation) approximately SSIM [Wang et al. IEEE Trans. Image Process. 13, 600-612 (2004)] > mean squared error NR [Wang et al. (2004) (unpublished)] > DCTune (NASA) > IQM (MITRE Corporation). The authors conclude that Case-PDM is very useful in MR image evaluation but that one should probably restrict studies to similar images and similar processing, normally not a limitation in image reconstruction studies.     

Analysis and localization of epileptic events using wavelet packets.

This article compares results obtained in previous studies using time-frequency representations (Wigner-Ville, Choi-Williams and Parametric) and the wavelet transform with those obtained with wavelet packet functions to show new findings about their quality in the analysis of ECoG recordings in human intractable epilepsy: data from 21 patients have been analyzed and processed with four types of wavelet functions, including Orthogonal, Biorthogonal and Non-Orthogonal basis. These functions were compared in order to test their quality to represent spikes in the ECoG. The energy based on the wavelet coefficients to different scales was also calculated. The best results were found with the biorthogonal-6.8 wavelet on 5-7 scales, which gave 0.92 sensitivity, but with a high percentage of false positives; this representation was highly correlated with spike events on time and duration. To improve these results we have studied the wavelet packet coefficients energy. We found that reconstruction wavelet packet coefficients at 4 and 9 nodes contain significant information to characterize the spike event. These nodes' reconstruction coefficients were multiplied and this product was highly correlated with spikes events on time and duration. With this procedure we improved the sensitivity up to 0.96 with the same biorthogonal-6.8 wavelet at four levels. With this technique we do not sacrifice computation time: 896 samples are processed at only 0.16 s, so that it is possible to show the spike scattering path on line, because 896 samples (7 s)/16 channels are processed at 3.13 s.     

Ultrasound grayscale image compression with JPEG and wavelet techniques

The purpose of the study was to evaluate the effects of lossy compression on grayscale ultrasound images to determine how much compression can be applied while still maintaining images that are acceptable for diagnostic purposes. The study considered how the acquisition technique (video frame-grabber versus directly acquired in digital form) influences how much compression can be applied. For directly acquired digital images, the study considered how text (that is burned into the image) affects the compressibility of the image. The lossy compression techniques that were considered include JPEG and a Wavelet algorithm using set partitioning in hierarchical trees (SPIHT).     

基于小波分析的医学图像压缩技术进展

作为图像存诸和传输系统(picture archiving & communication system,PACS)的关键技术之一,医学图像压缩算法的优劣对PACS的性能有着重要的影响,小波分析由于其多分辨率分析特性而在医学图像压缩中得到了广泛应用.从小波变换医学图像压缩、小波包变换医学图像压缩和多小波变换医学图像压缩三个方面综述了小波医学图像压缩方法及其进展,总结对比了这些方法的优点和缺陷,并针对其不足之处提出了改进方向.     

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.     

Performance analysis of a new semiorthogonal spline wavelet compression algorithm for tonal medical images

Lossy image compression is thought to be a necessity as radiology moves toward a filmless environment. Compression algorithms based on the discrete cosine transform (DCT) are limited due to the infinite support of the cosine basis function. Wavelets, basis functions that have compact or nearly compact support, are mathematically better suited for decorrelating medical image data. A lossy compression algorithm based on semiorthogonal cubic spline wavelets has been implemented and tested on six different image modalities (magnetic resonance, x-ray computed tomography, single photon emission tomography, digital fluoroscopy, computed radiography, and ultrasound). The fidelity of the reconstructed wavelet images was compared to images compressed with a DCT algorithm for compression ratios of up to 40:1. The wavelet algorithm was found to have generally lower average error metrics and higher peak-signal-to-noise ratios than the DCT algorithm.     

Myoelectric signal compression using zero-trees of wavelet coefficients

Recent progress in the diagnostic use of the myoelectric signal for neuromuscular diseases, coupled with increasing interests in telemedicine applications, mandate the need for an effective compression technique. The efficacy of the embedded zero-tree wavelet compression algorithm is examined with respect to some important analysis parameters (the length of the analysis segment and wavelet type) and measurement conditions (muscle type and contraction type). It is shown that compression performance improves with segment length, and that good choices of wavelet type include the Meyer wavelet and the fifth order biorthogonal wavelet. The effects of different muscle sites and contraction types on compression performance are less conclusive.A comparison of a number of lossy compression techniques has revealed that the EZW algorithm exhibits superior performance to a hard thresholding wavelet approach, but falls short of adaptive differential pulse code modulation. The bit prioritization capability of the EZW algorithm allows one to specify the compression factor online, making it an appealing technique for streaming data applications, as often encountered in telemedicine.     

MR脑图像组织分割的方法

图像分割是一个经典难题,至今没有一个通用的有效分割方法,也不存在一个分割的评价标准.应用MRI图像进行脑组织自动和有效的分割对诊断、病理研究、放疗和外科手术计划的制定、手术导航、计算机辅助诊断等各方面均有着极其重要的意义.磁共振脑图像比普通图像要复杂,组织边界不清晰,用通用方法无法实现良好的分割.对目前应用于脑组织分割并取得良好效果的分割方法给予综合论述,并分析了脑组织分割的发展.     

MR脑图像组织分割的方法

图像分割是一个经典难题,至今没有一个通用的有效分割方法,也不存在一个分割的评价标准。应用MRI图像进行脑组织自动和有效的分割对诊断、病理研究、放疗和外科手术计划的制定、手术导航、计算机辅助诊断等各方面均有着极其重要的意义。磁共振脑图像比普通图像要复杂,组织边界不清晰,用通用方法无法实现良好的分割。对目前应用于脑组织分割并取得良好效果的分割方法给予综合论述,并分析了脑组织分割的发展。     

ECG data compression using optimal non-orthogonal wavelet transform

This paper introduces an effective technique for the compression of electrocardiogram (ECG) signals. The technique is based on a new class of non-orthogonal discrete wavelet transform (DWT). The performance of ECG compression algorithm is measured by its ability to minimize distortion while retaining all clinically significant features of the signal. The percent root-mean square difference (PRD) is used as an accepted standard for measuring the signal distortion. However, there is no standard for measuring the clinically significant features retained after signal reconstruction. The coefficients of the DWT are calculated such that the square of the difference between the original signal and the reconstructed one is minimum in least mean square sense. The resulting transforms deal with signals of arbitrary lengths; that means the signal length is not restricted to be a multiple of power of 2. Numerical results comparing the performance of the constructed non-orthogonal transform with that of W-transform and Daubechies D(4) orthogonal transform are given. These results show that, independent of signal length, the decomposition of the signal up to the fourth level is sufficient for getting minimum PRD. In addition, the proposed technique yields the lowest PRD compared to the other two algorithms and for a compression ratio less than 10 the optimal transform can be obtained for only one ECG period. However, for a higher compression ratio the PRD is smaller for long signals.     

Ultrasound and angio image compression by cosine and wavelet transforms.

The investigation results for improving lossy compression techniques for ultrasound and angio images are presented. The goal was to determine where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that the wavelet transform outperforms the discrete cosine transform applied to ultrasound and angio images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterised by a content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to an algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images. A set of recommendations for acceptable compression ratio for different medical image modalities was developed. It was carried out on the base of compression study performed by the group of angiologists and cardiologists.     

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.     

Integer filters for image processing

In the paper Lynn's fast integer filtering methods are extended to two-dimensional image processing applications. Examples are included which verify the speed and the quality of the filtering for various types of filters. Initial condition requirements for one-dimensional integer filters are also extended to the two-dimensional case.     

ECG compression using wavelet transform and particle swarm optimization

A new adaptive thresholding mechanism to determine the significant wavelet coefficients of an electrocardiogram (ECG) signal is proposed. It is based on estimating thresholds for different sub-bands using the concept of energy packing efficiency (EPE). Then thresholds are optimized using the particle swarm optimization (PSO) algorithm to achieve a target compression ratio with minimum distortion. Simulation results on several records taken from the MIT-BIH Arrhythmia database show that the PSO converges exactly to the target compression after four iterations while the cost function achieved its minimum value after six iterations. Compared to previously published schemes, lower distortions are achieved for the same compression ratios.     

ECG compression using wavelet transform and particle swarm optimization

A new adaptive thresholding mechanism to determine the significant wavelet coefficients of an electrocardiogram (ECG) signal is proposed. It is based on estimating thresholds for different sub-bands using the concept of energy packing efficiency (EPE). Then thresholds are optimized using the particle swarm optimization (PSO) algorithm to achieve a target compression ratio with minimum distortion. Simulation results on several records taken from the MIT-BIH Arrhythmia database show that the PSO converges exactly to the target compression after four iterations while the cost function achieved its minimum value after six iterations. Compared to previously published schemes, lower distortions are achieved for the same compression ratios.     

An effective coding technique for the compression of one-dimensional signals using wavelet transforms

This paper introduces an effective technique for the compression of one-dimensional signals using wavelet transforms. It is based on generating a binary stream of 1s and 0s that encodes the wavelet coefficients structure (i.e., encodes the locations of zero and nonzero coefficients). A new coding algorithm, similar to the run length encoding, has been developed for the compression of the binary stream. The compression performances of the technique are measured using compression ratio (CR) and percent root-mean square difference (PRD) measures. To assess the technique properly we have evaluated the effect of signal length, threshold levels selection and wavelet filters on the quality of the reconstructed signal. The effect of finite word length representation on the compression ratio and PRD is also discussed. The technique is tested for the compression of normal and abnormal electrocardiogram (ECG) signals. The performance parameters of the proposed coding algorithm are measured and compression ratios of 19:1 and 45:1 with PRDs of 1% and 2.8% are achieved, respectively. At the receiver end, the received signal is decoded and inverse transformed before being processed. Finally, the merits and demerits of the technique are discussed.     

Three-dimensional image registration of MR proximal femur images for the analysis of trabecular bone parameters

This study investigated the feasibility of automatic image registration of MR high-spatial resolution proximal femur trabecular bone images as well as the effects of gray-level interpolation and volume of interest (VOI) misalignment on MR-derived trabecular bone structure parameters. For six subjects in a short-term study, a baseline scan and a follow-up scan of the proximal femur were acquired on the same day. For ten subjects in a long-term study, a follow-up scan of the proximal femur was acquired 1 year after the baseline. An automatic image registration technique, based on mutual information, utilized a baseline and a follow-up scan to compute transform parameters that aligned the two images. In the short-term study, these parameters were subsequently used to transform the follow-up image with three different gray-level interpolators. Nearest-neighbor interpolation and B-spline approximation did not significantly alter bone parameters, while linear interpolation significantly modified bone parameters (p<0.01). Improvement in image alignment due to the automatic registration for the long-term and short-term study was determined by inspecting difference images and 3D renderings. This work demonstrates the first application of automatic registration, without prior segmentation, of high-spatial resolution trabecular bone MR images of the proximal femur. Additionally, inherent heterogeneity in trabecular bone structure and imprecise positioning of the VOI along the slice (anterior-posterior) direction resulted in significant changes in bone parameters (p<0.01). Results suggest that automatic mutual information registration using B-spline approximation or nearest neighbor gray-level interpolation to transform the final image ensures VOI alignment between baseline and follow-up images and does not compromise the integrity of MR-derived trabecular bone parameters used in this study.     

Medical image compression using 3-D Hartley transform

In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modalities, namely, magnetic resonance images and X-ray angiograms and the performance results are compared with those of 3-D discrete cosine and Fourier transforms using the parameters such as PSNR and bit rate. It is shown that the 3-D discrete Hartley transform is better than the other two transforms for magnetic resonance brain images whereas for the X-ray angiograms, the 3-D discrete cosine transform is found to be superior.     

Modified SPIHT wavelet compression for ECG signal

This paper presents a modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression. Two more steps in the existing technique have been added to achieve higher compression ratio (CR) and lower percentage rms difference (PRD). The method has been tested on selected records from the MIT-BIH arrhythmia database. Even with two more steps, the method retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.