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PubMed1986

Image transmission and data compression. Two key elements of an electronic imaging department.

Growing numbers of diagnostic images are being produced electronically and filmed only for storage, or sometimes also for interpretation. However, in most imaging centers, transmission of images from one location to another is still being accomplished manually. Almost 40 years ago, x-rays were first transmitted over telephone lines, and this form of "teleradiology" has been increasingly used to link isolated hospitals and clinics to larger facilities so that radiologists can interpret the images. Continuing improvement of the various electronic image-processing technologies has produced faster means of transmission, and increasingly sophisticated computer algorithms for "compressing" the image data have further improved both transmission speed and the quality of the transmitted images. To date, only a few imaging research centers and teaching hospitals have integrated these techniques into a comprehensive picture archiving and communications system (PACS) or digital image management system (DIMS), and a number of factors are restraining these systems' spread. However, electronic transmission of images for interpretation by specialists at a central facility is likely to increase, and truly "filmless" radiology may someday become a reality, if image transmission and data compression techniques are eventually determined to create images of acceptable quality in all imaging modalities.

Computer Communication Networks↗
PubMed1990

ECG data compression techniques--a unified approach.

A broad spectrum of techniques for electrocardiogram (ECG) data compression have been proposed during the last three decades. Such techniques have been vital in reducing the digital ECG data volume for storage and transmission. These techniques are essential to a wide variety of applications ranging from diagnostic to ambulatory ECG's. Due to the diverse procedures that have been employed, comparison of ECG compression methods is a major problem. Present evaluation methods preclude any direct comparison among existing ECG compression techniques. The main purpose of this paper is to address this issue and to establish a unified view of ECG compression techniques. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are: ECG differential pulse code modulation and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods briefly presented, include: Fourier, Walsh, and K-L transforms. The theoretical basis behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, differential pulse code modulation (DPCM), and entropy coding methods. The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

Algorithms↗
PubMed1993

Data compression of the exercise ECG using a Kohonen neural network.

The goal for any data compression scheme is to maximize compression while minimizing distortion. This is particularly true for measurement sensitive electrocardiographic data. Many different approaches have been taken to achieve this goal. One common technique used in image and speech data compression, vector quantization, was selected for this study. Central to vector quantization is the creation of a codebook of vectors. Creating the best possible codebook will enable the best possible data compression. A neural network was used to create a codebook of vectors that attempt to span the low-frequency data space. Since these vectors are potentially the less critical areas of the electrocardiographic signal, less important information will be subjected to increases in distortion. The Kohonen paradigm used in this study is an unsupervised neural network that adapts the codebook vectors based on distance measurements and controls the scope of the changes based on time. This network has been shown to work well with image and speech data, but to the author's knowledge has not been used on electrocardiographic data. The compression of the signal comes from inserting the address of the codebook vector that best represents the original vector in place of the vector. A test is first performed to determine if the distortion between the original and the replacement vector is within a present limit. If it is, the address is inserted. If the distortion is too large the original vector will be retained. Typically, the QRS segment and possibly the T segment will be preserved. The new compressed file can be further reduced by lossless techniques to increase the compression ratio.(ABSTRACT TRUNCATED AT 250 WORDS)

Diagnosis, Computer-Assisted↗
PubMed1997

Effect of lossy data compression on quantitative coronary measurements.

With the accepted use of (lossy) data compression at low compression factors (2, 3 and 4 on the Philips DCI), the question was posed whether higher lossy compression ratios can also be used without statistically affecting the results of quantitative coronary arteriography. In this study the influence of two data compression schemes (LOT and JPEG) at three different compression factors (5, 8 and 12) on coronary measurements was assessed with the Automated Coronary Analysis (ACA) package. A series of 30 original acquired digital images were compressed and decompressed at the different factors, and together with the original non-compressed images processed using the ACA package. In these images a total of 37 obstructed coronary segments were analyzed twice to assess the intra-observer variabilities in the obstruction and reference diameters and in the percent diameter stenosis. The results of the first and second measurements in each image were averaged, and from the differences in corresponding images with different compression ratios, the inter-compression variability was obtained. The results show that the intra-observer systematic errors in the absolute diameters are all small (< 0.07 mm), and statistically not significantly different. The intra-observer random errors for the compressed/decompressed series, however, were all larger (up to 0.21 mm) than for the original series (< 0.13 mm). Statistically significant differences in the intra-observer random errors were found for the JPEG compression scheme at a compression ratio of 5 and for the LOT scheme at a compression ratio of 12. The inter-compression systematic errors in the absolute diameter measurements were also small (< 0.07 mm) and not significant, while the random errors were found to be high in the range between 0.23 mm and 0.31 mm. Given the higher intra-observer variabilities for the compressed/decompressed image series as compared to original images, and the fact that all inter-compression variabilities were found to be so high, we must conclude that the higher compression ratios affect the results of QCA in a negative sense. In conclusion, the use of lossy data compression with JPEG or LOT compression schemes at ratios 5, 8 and 12 must be discouraged for QCA.

Algorithms↗
PubMed1998

Clinical evaluation of irreversible data compression for computed radiography of the chest.

Efficient data compression is essential for practical daily operation of computed radiography (CR) systems. In this study the clinical applicability of type III irreversible high data compression using an FCR 9501 chest unit (Fuji Photo Film, Tokyo, Japan) was evaluated. Sixty-eight normal and 93 various abnormal cases, with an additional 15 cases of lung cancers with solitary lung nodules, were selected from the file. A pair of hard copies of original images and images reconstructed using type III compression was made for each case. Six radiologists evaluated the image quality by visual rating and receiver operating characteristic (ROC) curve analysis. For all five anatomic regions of normal cases, "original equal to compressed" was the most common response, followed by "original significantly better than compressed." When abnormal cases were evaluated for diagnostic information, there was no significant difference between the compressed and original images. ROC curve analysis on lung nodules with lung cancer showed no significant difference between the two. Compressed CR images using the type III irreversible technique are clinically applicable and acceptable despite slight degradation of image quality.

Adenocarcinoma↗
PubMed1997

EEG data compression techniques.

In this paper, electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed. Data compression permits one to achieve significant reduction in the space required to store signals and in transmission time. The Huffman coding technique in conjunction with derivative computation reaches high compression ratios (on average 49% on Holter and 58% on EEG signals) with low computational complexity. By exploiting this result a simple and fast encoder/decoder scheme capable of real-time performance on a PC was implemented. This simple technique is compared with other predictive transformations, vector quantization, discrete cosine transform (DCT), and repetition count compression methods. Finally, it is shown that the adoption of a collapsed Huffman tree for the encoding/decoding operations allows one to choose the maximum codeword length without significantly affecting the compression ratio. Therefore, low cost commercial microcontrollers and storage devices can be effectively used to store long Holter EEG's in a compressed format.

Algorithms↗
PubMed1996

Detection of subtle interstitial abnormalities of the lungs on digitized chest radiographs: acceptable data compression ratios.

OBJECTIVE: To determine acceptable compression ratios for digital radiography, we evaluated the effect of data compression on the detection of subtle interstitial lung abnormalities using digitized chest radiographs. MATERIALS AND METHODS: Screen-film chest radiographs of 38 patients with subtle interstitial lung abnormalities and 40 patients with normal lung parenchyma were digitized (spatial resolution, 0.175 mm; 2000 x 2000 pixels; 10 bits per pixel) and compressed with the discrete cosine transform method at ratios of 10:1, 20:1, and 30:1. Five chest radiologists and five radiology residents examined the uncompressed and compressed digital images and rates the presence of interstitial lung abnormalities with a five-level scale of confidence. Results were analyzed by receiver operating characteristic methods. RESULTS: Overall, the interpretation of images with a compression ratio of 30:1 was significantly less accurate than that of uncompressed images (p < .05). For the five chest radiologists, interpretation of images with a compression ratio of 20:1 or 30:1 was significantly less accurate than that of uncompressed images (p < .05). However, for the five residents, no significant difference between interpretations of compressed and uncompressed images was noted (p > or = .05). CONCLUSION: These results suggest that a 10:1 data compression ratio does not influence the detection of subtle interstitial lung abnormalities. However, information that is lost with a 20:1 data compression ratio might be essential for interpretation by experienced chest radiologists.

Adult↗
PubMed1994

ECG data compression with wavelet and discrete cosine transforms.

When applying transform techniques in data compression, an efficient approximation of the original signal using fewer transform coefficients is desired. The Discrete Cosine Transform (DCT) has been an effective tool in such applications. It decomposes a signal into a set of sinusoidal waveforms that are global in time. The DCT is not as efficient for signals with only local variations. The Wavelet Transform (WT) is a new technique that can decompose a signal into a set of small waveforms called wavelets. These wavelets possess local supports in the time domain, which makes the WT suitable for representing signals with local variations. Even though the ECG is dominated by low frequencies, its QRS complex exhibits strong localized characteristics. In this study, the effectiveness of the two transforms in compressing ECG data is investigated. Having noted the weakness of each transform, the two techniques are combined in two ways to compress the ECG data. It is observed that with the new techniques, better visualization quality can be achieved with the same total number of transform coefficients.

Electrocardiography↗
PubMedDate not supplied

Data compression for storage of resting ECGs digitized at 500 samples/second.

Data compression of resting electrocardiograms (ECGs) digitized at 500 samples per second (sps) is presented. Tradeoffs between the fidelity of reconstructed data and the overall compression are examined. Data of the median (average) complexes are retained at 500 sps and full resolution and encoded only to reduce redundancy. The raw data for rhythm analysis are evaluated for lowpass filtering and down-sampling (decimation) and requantization. After subtracting the medians from the raw data, the resulting residue signal is examined in detail for data reduction and encoding. Various options for compression of the residue signal are presented. Specific issues examined are the acceptable decimation and requantization of the residue signal. Another important aspect evaluated is the bimodal decimation of the QRS and the rest of the cardiac complex. Here, the QRS complexes are kept at 500 sps and the rest of the data decimated to lower sampling rates. This novel approach reduces data distortion while achieving significant compression. Details of the compression scheme and its evaluation on uncompressed 500-sps ECGs from the European Common Standards for Electrocardiography (CSE) database (128 ECGs with normal sinus rhythm, atrioventricular blocks, atrial fibrillation and flutter, ventricular arrhythmias, and excessive noise) are elucidated. Performance indexes [root mean square (RMS) error, percent RMS difference, normalized RMS difference, maximum peak error, and compression ratio] are computed. To validate the compression method, qualitative evaluation was performed by two physicians overreading the ECGs by comparing the reconstructed waveforms with the original uncompressed data. The median data are retained at 500 sps and full precision. For rhythm data, the bimodal decimation of the residue signal to 125 sps at 10 microvolts resolution preserved the fidelity of the ECG signals well, while giving good data compression. Abnormal atrial activity was well preserved and the QRS was retained without distortion. The average size of a 10-sec compressed ECG with the medians is around 4.5 kilobytes.

Diagnosis, Computer-Assisted↗
PubMed2005

Ultrasonic data compression via parameter estimation.

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.

Algorithms↗
PubMed1988

Data compression: 8-dimensional flow cytometric data processing with 28K addressable computer memory.

A method of data analysis for flow cytometry is presented which enables up to eight-dimensional data to be handled by a microcomputer with 28K addressable plus a further 32K non-addressable memory. The multi-parameter coordinates are coded into single numbers using a minimal modification of the array vector mapping equation. These code numbers, each of which corresponds to a given set of coordinates, and then ranked in ascending order according to magnitude and the frequency of each code number is found. Following this step the code is then decoded by integer arithmetic into its original coordinates which are then packed, together with the frequency, into two, three or four 16-bit words depending on the dimensionality of the data set. A five-dimensional data set is used as the illustration. Three regions were set on one two-dimensional data space and the five mono-dimensional histograms, plus a different bivariate distribution, were extracted in a single pass through the processed data file. In addition to considerable space saving the technique has two further attributes, namely, increased speed with which the user can appreciate multiparameter data and the ability to analyse such data sets with a microcomputer.

Algorithms↗
PubMed1991

EEG data compression with source coding techniques.

A data compression algorithm for the EEG, derived from the adaptive pulse code modulation scheme, is described where the consecutively computed differences are coded by passing them through a quantizer possessing only a few levels; the range of these levels is adapted to local signal statistics. Three different versions of the algorithm with data reduction up to 75% are presented. The system was validated using several multichannel-routine EEG recordings with both visual evaluation and computation of signal-to-noise ratios.

Algorithms↗
PubMed1992

Data compression of electrocardiograms for long-time digital recording in IC memory.

To realize a digital recorder capable of long-time recording of electrocardiograms (ECGs) in IC memories, the methods of data compression were studied and the accuracy of reconstructed ECGs from the compressed ones was evaluated. Concretely, the variant of MSAPA was used as a data compression algorithm in order to store ECGs in IC memories for 12-24 h. The fabricated digital recorder used an 8 bit CPU with the function of data compression and was capable of operating in real time at a sampling rate of 200 Hz. With bit compression, 24 samples of American Heart Association ECGs were compressed to an average value of 16.8% under the condition of an error limit of 30 microV (120 microV in QRS); the average maximum recording time in 1 Mbyte IC memories was 8.5 h. In a comparison of the reproduction accuracy and errors produced by a small analog tape recorder with those produced by the digital method, the digital system was shown to be superior to the analog system.

Algorithms↗
PubMed1998

[An algorithm for ECG data compression and its real-time realization with single chip computer].

ECG data compression has been greatly improved since 1960 s. Although the error of compression can be controlled accurately, the reported algorithm at present can not be processed in real-time with a microcomputer. In this paper an algorithm for ECG data compression is expounded which can be realized in real-time with a single chip computer. The algorithm has not only adjustable precision but also an average compression rate of 10.34, especially a pretty high compression ratio for frequent arrhythmic patients.

Algorithms↗
PubMedDate not supplied

Human motion capture data compression by model-based indexing: a power aware approach.

Human Motion Capture (MoCap) data can be used for animation of virtual human-like characters in distributed virtual reality applications and networked games. MoCap data compressed using the standard MPEG-4 encoding pipeline comprising of predictive encoding (and/or DCT decorrelation), quantization, and arithmetic/Huffman encoding, entails significant power consumption for the purpose of decompression. In this paper, we propose a novel algorithm for compression of MoCap data, which is based on smart indexing of the MoCap data by exploiting structural information derived from the skeletal virtual human model. The indexing algorithm can be fine-controlled using three predefined quality control parameters (QCPs). We demonstrate how an efficient combination of the three QCPs results in a lower network bandwidth requirement and reduced power consumption for data decompression at the client end when compared to standard MPEG-4 compression. Since the proposed algorithm exploits structural information derived from the skeletal virtual human model, it is observed to result in virtual human animation of visually acceptable quality upon decompression.

Algorithms↗
PubMed1998

Clinical evaluation of irreversible data compression for computed radiography of the hand.

We evaluated the clinical utility of high data compression for computed radiography (CR) of the hand. Paired original and compressed CR images were obtained using an FCR 9000 system in 35 patients with various abnormalities of the hand. For the paired original and compressed images, we compared the depiction of the bone cortex, bone trabeculae, soft-tissue, and the margin of the abnormalities. The overall impressions for detectability of the abnormalities and for diagnostic capability were also evaluated. Poorer image quality of the compressed images was noted for most of the structures, but there was no significant difference between the original and compressed images as for the overall impressions for detectability of the abnormalities or for diagnostic capability. We conclude that a high data compression may be clinically acceptable with caution in radiography of the hand.

Female↗
PubMed2005

Optimal erasure protection strategy for scalably compressed data with tree-structured dependencies.

This paper is concerned with the transmission of scalably compressed data sources over lossy channels. Specifically, this paper is concerned with packet networks or, more generally, erasure channels. Previous work has generally assumed that the source elements form linear dependencies. The contribution of this paper is an unequal erasure protection algorithm which is able to take advantage of scalable data with more general dependency structures. In particular, the proposed scheme is adapted to data with tree-structured dependencies. The source elements are allocated to clusters of packets according to their dependency structure, subject to constraints on packet size and channel code-word length. Given a packet cluster arrangement, source elements are assigned optimal channel codes subject to a constraint on the total transmission length. Experimental results confirm the benefit associated with exploiting the actual dependency structure of the data.

Algorithms↗
PubMed1996

Telephone transmission of 20-channel digital electroencephalogram using lossless data compression.

BACKGROUND: The use of telecommunications for computer-assisted transmission of neurophysiological signals is a relatively new practice. With the development of digital technology, it is now possible to record electroencephalograms (EEGs) in digital form. Previous reports have demonstrated the possibility of real-time telephone transmission of a limited number of EEG channels. OBJECTIVES: To assess the effectiveness of specific data-compression software to improve the transmission of digital 20-channel EEG records over ordinary public telephone lines. METHODS: A prototype system was built to transmit digital EEG signals from one computer to another using two 14.4-kbps modems and proprietary lossless data-compression software. RESULTS: Forty compressed digital EEG records of 20 channels each were sent from different locations at variable distances using "plain old telephone service" (POTS). The mean compression ratio was 2.2 to 2.8:1 using a sampling frequency of 128 Hz and 2.8:1 at a sampling rate of 256 Hz. Transmission time was reduced proportionately. CONCLUSION: Although this study used a store-and-forward approach, the results suggest that it may be possible to transmit a large number of compressed EEG channels in real time using data compression.

Electroencephalography↗