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PubMed1994

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.

Algorithms↗
PubMed2006

High-quality MRC document coding.

The mixed raster content (MRC) model can be used to implement highly effective document compression algorithms. MRC document coders are typically based on the use of a binary mask layer that efficiently encodes the text and graphic content. However, while many MRC-based methods can yield much higher compression ratios than conventional color image compression methods, the binary representation tends to distort fine document details, such as thin lines and text edges. In this paper, we propose a method for encoding and decoding the binary mask layer that substantially improves the decoded document fidelity of text and graphics at a fixed bit rate. This method, which we call resolution-enhanced rendering (RER), works by adaptively dithering the encoded binary mask, and then applying a nonlinear predictor to decode a gray level mask at the same resolution. Both the dithering and nonlinear prediction algorithms are jointly optimized to produce the minimal distortion rendering. In addition, we introduce a second method, interpolative RER (IRER), which incorporates interpolation into the MRC decoder. The IRER method increases the compression ratio by allowing a high-resolution document to be coded at lower resolutions. We present experimental results illustrating the performance of our RER/IRER methods and comparing them to some existing MRC-based compression algorithms.

Algorithms↗
PubMed2006

ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds.

The modified embedded zero-tree wavelet (MEZW) compression algorithm for the one-dimensional signal was originally derived for image compression based on Shapiro's EZW algorithm. It is revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate. The EZW and MEZW algorithms apply the chosen threshold values or the expressions in order to specify that the significant transformed coefficients are greatly significant. Thus, two different threshold definitions, namely percentage and dyadic thresholds, are used, and they are applied for different wavelet types in biorthogonal and orthogonal classes. In detail, the MEZW and EZW algorithms results are quantitatively compared in terms of the compression ratio (CR) and percentage root mean square difference (PRD). Experiments are carried out on the selected records from the MIT-BIH arrhythmia database and an original ECG signal. It is observed that the MEZW algorithm shows a clear advantage in the CR achieved for a given PRD over the traditional EZW, and it gives better results for the biorthogonal wavelets than the orthogonal wavelets.

Algorithms↗
PubMed2002

Computed tomography diagnosis utilizing compressed image data: an ROC analysis using acute appendicitis as a model.

Using receiver-operating characteristic (ROC) methodology, the ability to diagnose acute appendicitis with computed tomography (CT) images displayed at varying levels of lossy compression was evaluated. Nine sequential images over the ileocecal region were obtained from 53 consecutive patients with right lower quadrant pain who were clinically suspected to have acute appendicitis. Thirty were proven surgically to have acute appendicitis, alternative diagnoses confirmed in 23. The image sets were subjected to a lossy wavelet-based compression algorithm "Embedded Predictive Wavelet Image Coder" (EPWIC). Compression levels were: none, 8:1, 16:1, and 24:1, resulting in 4 sets of images per patient. Image sets were randomized and evaluated separately by 4 body radiologists on a 1,024 x 768-pixel SVGA color PC monitor in 512 x 512 format. The readers were aware of the clinical suspicion of appendicitis but were unaware of the positive fraction of cases. Individual and combined reader ROC and c2 analyses of sensitivity, specificity, and accuracy were determined. For all readers, sensitivity decreases at 16:1 and 24:1 levels (P <0.01, P <0.001, respectively). Accuracy decreased at 24:1 levels (P <0.01). Specificity was unaffected. By ROC analysis there was statistically significantly decreased area under the curve at 24:1 levels (P <0.02) as compared with uncompressed images. Finite levels of lossy wavelet compression may be applied to CT images without compromising diagnostic performance.

Acute Disease↗
PubMed2006

The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds.

Linear model observers based on statistical decision theory have been used successfully to predict human visual detection of aperiodic signals in a variety of noisy backgrounds. However, some models have included nonlinearities such as a transducer or nonlinear decision rules to handle intrinsic uncertainty. In addition, masking models used to predict human visual detection of signals superimposed on one of two identical backgrounds (masks) usually include a number of nonlinear components in the channels that reflect properties of the firing of cells in the primary visual cortex (V1). The effect of these nonlinearities on the ability of linear model observers to predict human signal detection in real patient structured backgrounds is unknown. We evaluate the effect of including different nonlinear human visual system components into a linear channelized Hotelling observer (CHO) using a signal known exactly but variable (SKEV) task. In particular, we evaluate whether the rank order of two compression algorithms (JPEG versus JPEG 2000) and two compression encoder settings (JPEG 2000 default versus JPEG 2000 optimized) based on model observer signal detection performance in X-ray coronary angiograms is altered by inclusion of nonlinear components. The results show: 1) the simpler linear CHO model observer outperforms CHO model with the nonlinear components; 2) the rank order of model observer performance for the compression algorithms/parameters does not change when the nonlinear components are included. For the present task and images, the results suggest that the addition of the nonlinearities to a channelized Hotelling model may add complexity to the model observers without great impact on rank order evaluation of image processing and/or acquisition algorithms.

Algorithms↗
PubMed1998

Data compression for diffraction patterns.

Efficient coding (lossless) and compression (lossy) of diffraction patterns is important in protein crystallography experiments because of storage and transmission limitations. The goal is to reduce the bit-rate significantly while keeping diffraction peak intensity distortion at an acceptable level. This paper presents an overview of coding and compression techniques more or less adapted to such problems. A large part of this study is dedicated to time-frequency-transform based compression algorithms and some of their extensions. Wavelet based software has been developed and tested. Results are compared with the discrete cosine transform (DCT) and other classical algorithms. These tools seem attractive and very promising for analyzing and compressing signals with singularities and transient phenomena such as diffraction peaks. Tests were performed on a standard protein crystallography data set coming from the CCD detector of D2AM beamline at the European Synchrotron Radiation Facility at Grenoble. These were compressed with DCT and wavelet-based algorithms. It appears that alterations of the result of the processing of restored images remain very weak for compression rates up to 10. These preliminary results indicate that the proposed wavelet method is a good standard technique for efficient compression of diffraction patterns.

Algorithms↗
PubMed1998

Perceived fidelity of compressed and reconstructed radiological images: a preliminary exploration of compression, luminance, and viewing distance.

The authors' goal was to explore the impact of image compression algorithm and ratio, image luminance, and viewing distance on radiologists' perception of reconstructed image fidelity. Five radiologists viewed 16 sets of four hard-copy chest radiographs prepared for secondary interpretation. Each set included one uncompressed, and three compressed and reconstructed images prepared using three different algorithms but the same compression ratio. The sets were prepared using two subjects, four compression ratios (10:1, 20:1, 30:1, 40:1), and two luminance levels (2,400 cd/m2, standard lightbox illumination, and 200 cd/m2, simulating a typical CRT display). Readers ranked image quality and evaluated obviousness and clinical importance of differences. Viewing distances for image screening, inspection, and comparison were recorded. At 10:1 compression, the compressed and uncompressed images were nearly indistinguishable; the three algorithms were very similar, and differences were rated "not obvious" and "not important." At higher compression, readers consistently preferred uncompressed images, with notable differences between algorithms. The obviousness and clinical importance of differences were rated higher at lightbox luminance. Viewing distances appeared to be idiosyncratic.

Adolescent↗
PubMed2003

Amorphous silicon, flat-panel, x-ray detector versus storage phosphor-based computed radiography: contrast-detail phantom study at different tube voltages and detector entrance doses.

RATIONALE AND OBJECTIVES: Evaluation of the contrast-detail performance of an active-matrix flat-panel x-ray detector in comparison with a storage phosphor system with special regard to the potential of dose reduction. METHODS: A digital x-ray detector based on cesium iodide (CsI) and amorphous silicon (a-Si) technology was compared with a fifth-generation storage phosphor system. A lucite plate with 36 drilled holes of varying diameters and depths was used as contrast-detail phantom. At 45 kVp, 70 kVp, and 113 kVp, images at 8 different detector entrance doses ranging between 0.3 microGy and 40 microGy were obtained. On soft-copy displays, 3 masked observers evaluated the detectability of each aperture in each image according to a 5-point scale. The mean sum scores of corresponding images were compared. RESULTS: For all tube voltages and detector entrance doses, the images obtained with the CsI/a-Si detector resulted in better observer contrast-detail performance as compared with the images of the storage phosphor system. The CsI/a-Si system allowed a calculated dose reduction of 39% at 45 kVp, 68% at 70 kVp, and 81% at 113 kVp as compared with the storage phosphor system, without loss of contrast-detail detectability. CONCLUSIONS: Under the conditions of the chosen experimental design, the CsI/a-Si system provided a superior contrast-detail performance as compared with the storage phosphor system. The potential of dose reduction increased with rising tube voltage.

Cesium↗
PubMed1998

Wavelet versus JPEG (Joint Photographic Expert Group) and fractal compression. Impact on the detection of low-contrast details in computed radiographs.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate different lossy image compression algorithms in direct comparison. METHODS: Computed radiographs were reviewed after compression with Wavelet, Fractal, and Joint Photographic Expert Group (JPEG) algorithms. For receiver operating characteristic (ROC) analysis, 54 thoracic computed radiographs (31 showing pulmonary nodules) were compressed with a ratio of 1:60. Five images of a test-phantom were coded at 1:13. All images were reviewed on a PC. Uncompressed images were reviewed at a PC and at a radiologic workstation (with image processing). RESULTS: For thorax images, decrease of diagnostic accuracy was significant with Wavelets. Fractal performed worse than Wavelets. No ROC curve was observed for JPEG due to poor image quality. No diagnostic loss was noted comparing PC and Workstation review. For low-contrast details of the phantom, results of Wavelet compression were equal to uncompressed images. Fewer true positives and increased true negatives were noted with Wavelets though. Wavelets were superior to JPEG, and JPEG images were superior to Fractal. Workstation review was superior to PC review. CONCLUSIONS: Only Wavelets provided accurate review of low-contrast details at a compression of 1:13. Frequency filtering of Wavelets affects contrast even at a low compression ratio. JPEG performed better than Fractal at low and worse at high compression ratio.

Algorithms↗
PubMed2005

Compression and reconstruction of sorted PET listmode data.

BACKGROUND: In nuclear medicine data can be stored in histogram or listmode format. The most popular histogram format is the planar projection format. Due to the increase in detector blocks, the improved energy resolution and the trends towards time of flight, dynamic and gated imaging, it can be more appropriate to store the data in listmode format. The size of the storage in this format increases linearly with the number of properties (positions, energy, time info) while the histogram format increases exponentially. However, the datasize of listmode data also increases linearly with the number of coincidences. Due to the high number of counts in 3D PET this will lead to very large datasets. Therefore a good compression algorithm for listmode data is very important. METHODS: A sorting and compression method is proposed to reduce the amount of space needed to store the listmode dataset. One event is represented by one number without any information loss compared to the original listmode file. The next step is to sort all events into an array of increasing numbers. These data are compressed by the gzip routine. One of the advantages of 3D PET listmode reconstructions is that they result in a more uniform resolution across the field of view (FOV), which is not always true for other reconstruction algorithms. This improved resolution is shown for the listmode data of a gamma camera operating in PET mode. RESULTS: First the effect of positional accuracy in the listmode dataset is evaluated by comparing resolution in the reconstructions. It is shown that the highest accuracy is not necessary and a significant reduction in the size of the dataset can be obtained prior to lossless compression. A further reduction can be obtained by using the proposed sorting and compression techniques. It is shown that the storage space decreases linearly with the logarithm of the number of coincidences. The compression obtained by different acquisition matrices was compared. Finally it is shown that the 3D listmode reconstruction of sorted listmode data is faster because of improved cache behaviour. The method can be applied to any kind of listmode data. The compression factors will improve when the ratio of measured events to possible events increases.

Algorithms↗
PubMed2003

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.

Adult↗
PubMed1998

KLT-based quality controlled compression of single-lead ECG.

An electrocardiogram (ECG) compression algorithm based on a combination of the Karhunen-Loeve transform (KLT) and multirate sampling is introduced. The use of multirate sampling reduces KLT computational times to those reported for wavelet-packet-based compression techniques. A beat-by-beat quality controlled compression criterion is shown to be necessary to ensure clinically adequate reconstruction of each beat. The resulting quality controlled algorithm efficiently achieves compression rates of approximately 30-40:1 for the MIT-BIH database.

Algorithms↗
PubMed2006

Lossless compression of VLSI layout image data.

We present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4), which integrates the advantages of two very disparate compression techniques: context-based modeling and Lempel-Ziv (LZ) style copying. While the algorithm can be applied to many lossless compression applications, such as document image compression, our primary target application has been lossless compression of integrated circuit layout image data. These images contain a heterogeneous mix of data: dense repetitive data better suited to LZ-style coding, and less dense structured data, better suited to context-based encoding. As part of C4, we have developed a novel binary entropy coding technique called combinatorial coding which is simultaneously as efficient as arithmetic coding, and as fast as Huffman coding. Compression results show C4 outperforms JBIG, ZIP, BZIP2, and two-dimensional LZ, and achieves lossless compression ratios greater than 22 for binary layout image data, and greater than 14 for gray-pixel image data.

Algorithms↗
PubMed1997

[Digital filing system for medical image data].

To establish practical, effective medical digital image filing systems, various problems remain to be solved. The authors focused on 2 such problems of great importance: image data compression and standard specifications for digital data storage. From a variety of image compression algorithms now available, 2D-Color Doppler image compression was clinically investigated using the Joint Photographic Expert Group (JPEG) method. In low grade compression, the images decompressed showed no significant alterations in image quality, however, in higher grades of compression attained through irreversible coding in the JPEG method, the images showed subtle but significant alterations: emergence of 2 kinds of artifact, i.e., borderline-artifact produced in the DCT procedures and pseudo color spots. The reproducibility of colors was found to be altered by changing the quantization table in the JPEG procedure. It was thought necessary to choose a proper quantization table and examine the DC coefficients (number of pixels) in the DCT procedure to attain practical 2D-Color Doppler image compression by the JPEG method. Although attempts in Japan are being made to establish common standards for electronic data storage, based on MEDIS, in an effort to promote standardization there are many problems to be solved before practical use is possible. To ensure common availability as required by the technical standards, it is necessary to organize minimum-required specifications into common standards and make them open to the public.

Algorithms↗
PubMed2004

High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms.

Biomedical waveforms, such as electrocardiogram (ECG) and arterial pulse, always possess a lot of important clinical information in medicine and are usually recorded in a long period of time in the application of telemedicine. Due to the huge amount of data, to compress the biomedical waveform data is vital. By recognizing the strong similarity and correlation between successive beat patterns in biomedical waveform sequences, an efficient data compression scheme mainly based on pattern matching is introduced in this paper. The waveform codec consists mainly of four units: beat segmentation, beat normalization, two-stage pattern matching and template updating and residual beat coding. Three different residual beat coding methods, such as Huffman/run-length coding, Huffman/run-length coding in discrete cosine transform domain, and vector quantization, are employed. The simulation results show that our compression algorithms achieve a very significant improvement in the performances of compression ratio and error measurement for both ECG and pulse, as compared with some other compression methods.

Algorithms↗
PubMed2006

Evaluation of the effect of JPEG and JPEG2000 image compression on the detection of diabetic retinopathy.

AIMS: To compare the effect of classic Joint Photographic Experts Group (JPEG) and JPEG2000 compression algorithms on detection of diabetic retinopathy (DR) lesions. METHODS: In total, 45 colour fundus photographs obtained with a digital nonmydriatic fundus camera were saved in uncompressed Tagged Interchanged Files Format (TIFF) format (1.26 MB). They were graded jointly by two retinal specialists at a 1 month interval for soft exudates, hard exudates, macular oedema, newvessels, intraretinal microvascular abnormalities (IRMA), and retinal haemorrhages and/or microaneurysms. They were compressed to 118, 58, 41, and 27 KB by both algorithms and 24 KB by classic JPEG, placed in random order and graded again jointly by the two retina specialists. Subjective image quality was graded, and sensitivity, specificity, positive and negative predictive values, and kappa statistic were calculated for all lesions at all compression ratios. RESULTS: Compression to 118 KB showed no effect on image quality and kappa values were high (0.94-1). Image degradation became important at 27 KB for both algorithms. At high compression levels, IRMA and HMA detection were most affected with JPEG2000 performing slightly better than classic JPEG. CONCLUSION: Performance of classic JPEG and JPEG2000 algorithms is equivalent when compressing digital images of DR lesions from 1.26 MB to 118 KB and 58 KB. Higher compression ratios show slightly better results with JPEG2000 compression, but may be insufficient for screening purposes.

Algorithms↗
PubMed2005

Computed Quality Assessment of MPEG4-compressed DICOM Video Data.

Digital Imaging and Communication in Medicine (DICOM) has become one of the most popular standards in medicine. This standard specifies the exact procedures in which digital images are exchanged between devices, either using a network or storage medium. Sources for images vary; therefore there exist definitions for the exchange for CR, CT, NMR, angiography, sonography and so on. With its spreading, with the increasing amount of sources included, data volume is increasing, too. This affects storage and traffic. While for long-time storage data compression is generally not accepted at the moment, there are many situations where data compression is possible: Telemedicine for educational purposes (e.g. students at home using low speed internet connections), presentations with standard-resolution video projectors, or even the supply on wards combined receiving written findings. DICOM comprises compression: for still image there is JPEG, for video MPEG-2 is adopted. Within the last years MPEG-2 has been evolved to MPEG-4, which squeezes data even better, but the risk of significant errors increases, too. Within the last years effects of compression have been analyzed for entertainment movies, but these are not comparable to videos of physical examinations (e.g. echocardiography). In medical videos an individual image plays a more important role. Erroneous single images affect total quality even more. Additionally, the effect of compression can not be generalized from one test series to all videos. The result depends strongly on the source. Some investigations have been presented, where different MPEG-4 algorithms compressed videos have been compared and rated manually. But they describe only the results in an elected testbed. In this paper some methods derived from video rating are presented and discussed for an automatically created quality control for the compression of medical videos, primary stored in DICOM containers.

Algorithms↗
PubMed1991

Image data compression using a new floating-point digital signal processor.

A new dual-ported, floating-point, digital signal processor has been evaluated for compressing 512 and 1,024 digital radiographic images using a full-frame, two-dimensional, discrete cosine transform (2D-DCT). The floating point digital signal processor operates at 49.5 million floating point instructions per second (MFLOPS). The level of compression can be changed by varying four parameters in the lossy compression algorithm. Throughput times were measured for both 2D-DCT compression and decompression. For a 1,024 x 1,024 x 10-bit image with a compression ratio of 316:1, the throughput was 75.73 seconds (compression plus decompression throughput). For a digital fluorography 1,024 x 1,024 x 8-bit image and a compression ratio of 26:1, the total throughput time was 63.23 seconds. For a computed tomography image of 512 x 512 x 12 bits and a compression ratio of 10:1 the throughput time was 19.65 seconds.

Algorithms↗