Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Data Compression”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources
PubMed2002

Statistical mechanics of lossy data compression using a nonmonotonic perceptron.

The performance of a lossy data compression scheme for uniformly biased Boolean messages is investigated via methods of statistical mechanics. Inspired by a formal similarity to the storage capacity problem in neural network research, we utilize a perceptron of which the transfer function is appropriately designed in order to compress and decode the messages. Employing the replica method, we analytically show that our scheme can achieve the optimal performance known in the framework of lossy compression in most cases when the code length becomes infinite. The validity of the obtained results is numerically confirmed.

Journal Article↗
PubMed1994

Data compression for medical report archiving.

In a hospital environment, where large numbers of reports are compiled, data compression offers a method for substantially reducing the required disk space whilst protecting the confidentiality of patient data. An analysis is given of the most important parameters that influence compression of free-text data, to realize an efficient implementation of an archiving system for medical reports. An overall reduction of the required disk space by more than 50% can be attained, as well as supplementary level of protection. A slight increase in access time is thereby inevitable, but almost insignificant. Such an archiving system constitutes a necessary component for an intelligent information retrieval system. The access to information contained in medical documents (by means of natural language processing and artificial intelligence techniques) is considered to be one of the key issues in the field of medical informatics for the coming decades.

Archives↗
PubMed2003

A context-free data compression approach to measuring information transmission by action potentials.

Action potentials allow nervous systems to transmit information rapidly and efficiently over considerable distances, but what is the information they carry and how much can be carried by one neuron? Often, qualitative and vague descriptions are used, such as the firing rate representing intensity. Recent attempts to quantify information transmission by action potentials have concentrated on treating neurons as communication channels, whose information capacity can be estimated from their signal-to-noise ratios. However, this only indicates how much information could theoretically be carried, not the actual amount at any given time, and the ratio itself depends on assumptions about information coding. Here we introduce a different approach based on the concept of data compression, which has become familiar with the widespread use of digital computers and networks. Compression takes advantage of redundancy in a sequence of numbers to reduce its size, but allows it to be reconstructed later without error. We show that data compression by a context-free grammar can quantitatively estimate the real information content of action potential signals without any prior assumptions about coding, or knowledge of neural inputs. Measurements of information coding by mechanosensory neurons are used as examples, but a major advantage of this approach is its generality. It can estimate information transmission by any neuron whose output can be measured, regardless of neuronal type, connectivity or function.

Action Potentials↗
PubMed2001

Effectiveness of radiological image data compression.

This study was designed to evaluate effectiveness of radiological image data compression in terms of image quality and archiving material costs using DLT tapes, and to assess the relationship between loss of quality and cost savings. Six radiologists used Subjective Fidelity Criteria (SFC) in random fashion to evaluate the quality of 105 digitally acquired radiological images. In addition, 5 radiologists and 2 nonradiologists evaluated at random three phantom images exposed in conditions mimicking chest, bone, and colon examinations, displayed in five modes (a total of 15 images). Both patient images and phantom images were submitted to 3:1 (Ziv-Lempel method) and 10:1 compression (wavelet-based compression method). Cost information on material cost savings and the effect of compression on tape space requirements were compared. The results indicate that image quality was not degraded using either of the compression ratios. The interobserver proportion of agreement exceeded overwhelmingly the limit of a good proportion of agreement regarding each compression ratio and each image type. The divergence in the rest of the assessments was not consistent. The adoption of 10:1 compression would not bring a substantial decrease of archiving costs as compared to the total yearly operating costs, and especially as considering the consequences of possible image quality deterioration.

Chi-Square Distribution↗
PubMed2003

Estimating topological entropy via a symbolic data compression technique.

We estimate topological entropy via symbolic dynamics using a data compression technique called the context-tree weighting method. Unlike other symbolic dynamical approaches, which often have to choose ad hoc parameters such as the depth of a tree, the context-tree weighting method is almost parameter-free and infers the transition structure of the system as well as transition probabilities. Our examples, including a Markov model, the logistic map, and the Hénon map, demonstrate that the convergence is fast: one obtains the theoretically correct topological entropy with a relatively short symbolic sequence.

Journal Article↗
PubMed1997

Dynamic image data compression in spatial and temporal domains: theory and algorithm.

Advanced medical imaging requires storage of large quantities of digitized clinical data. These data must be stored in such a way that their retrieval does not impair the clinician's ability to make a diagnosis. In this paper, we propose the theory and algorithm for near (or diagnostically) lossless dynamic image data compression. Taking advantage of domain-specific knowledge related to medical imaging, the medical practice and the dynamic imaging modality, a compression ratio greater than 80:1 is achieved. The high compression ratios are achieved by the proposed compression algorithm through three stages: 1) addressing temporal redundancies in the data through application of image optimal sampling, 2) addressing spatial redundancies in the data through cluster analysis, and 3) efficient coding of image data using standard still-image compression techniques. To illustrate the practicality of the proposed compression algorithm, a simulated positron emission tomography (PET) study using the fluoro-deoxy-glucose (FDG) tracer is presented. Realistic dynamic image data are generated by "virtual scanning" of a simulated brain phantom as a real PET scanner. These data are processed using the conventional [8] and proposed algorithms as well as the techniques for storage and analysis. The resulting parametric images obtained from the conventional and proposed approaches are subsequently compared to evaluate the proposed compression algorithm. As a result of this study, storage space for dynamic image data is able to be reduced by more than 95%, without loss in diagnostic quality. Therefore, the proposed theory and algorithm are expected to be very useful in medical image database management and telecommunication.

Algorithms↗
PubMed1999

ECG data compression using cut and align beats approach and 2-D transforms.

A new electrocardiogram (ECG) data compression method is presented which employs a two dimensional (2-D) transform. This 2-D transform method utilizes the fact that ECG signals generally show two types of redundancies--between adjacent heartbeats and between adjacent samples. A heartbeat data sequence is cut and beat-aligned to form a 2-D data array. Any 2-D compression method can then be applied. Transform coding using the 2-D discrete cosine transform (DCT) [2-D DCT] is employed here as an example. Using selections from the MIT-BIH arrhythmia and Medtronic databases, results are presented that illustrate substantial improvement in compression ratio over one-dimensional methods for comparable percent root-mean-square difference (PRD).

Algorithms↗
PubMed2002

Tele-endoscopy: influence of data compression, bandwidth and simulated impairments on the usability of real-time digital video endoscopy transmissions for medical diagnoses.

BACKGROUND AND STUDY AIMS: Real-time digital video transmission (rtDVT) at an acceptable quality through networks has been possible for several years. This technique can be used for telemedical applications, such as tele-endoscopy. The hypotheses of the present study were that the quality of real-time DVT depends on the technical equipment used, and that the resulting image quality influences the usability of the system for diagnostic purposes. MATERIALS AND METHODS: An experimental network was established between two German referral endoscopy centers, using the Asynchronous Transfer Mode (ATM) protocol. At first, rtDVTs of routine gastrointestinal video endoscopies were transferred through the network for prospective evaluation of the feasibility of the technical equipment and its usability for diagnostic tele-endoscopy, based on the video image quality. Secondly, the image quality and usability for correct telemedical diagnosis were evaluated prospectively in a double-blind experimental setting in relation to variations in the methods of data compression used, transmission bandwidths, and simulated transmission errors. Fourteen endoscopists evaluated 27 variations of an endoscopic video sequence. RESULTS: Compression with the Moving Picture Expert Group 2 (MPEG2 [4 : 2 : 2]) standard, the ATM protocol, and a bandwidth of 40 megabits per second (Mb/s) were used successfully in 40 routine tele-endoscopies for practical evaluation. Doctors were able to handle the system with ease, and its availability was 100%. There were no detectable differences between the original video image and the transferred image, and the images were usable for diagnosis in all cases. The set-up used clinically was therefore considered to provide the optimal conditions for comparisons in the experimental part of the study. Experimentally, any technical variation was found to cause a reduction in the overall image quality and hence a reduction in diagnostic usability: compression algorithm (MPEG2 [4 : 2 : 2] vs. others: P = 0.001), bandwidth (> or = 8 vs. < 8 Mb/s: P = 0.001), and error rate (10 (-8) vs. 10 (-7): P = 0.001). CONCLUSIONS: rtDVT using MPEG2 [4 : 2 : 2] compression and a bandwidth of 40 Mb/s did not effectively differ from the original video images in routine tele-endoscopy. The qualitative requirements in diagnostic video endoscopy, however, are obviously much higher than previously assumed, since experienced endoscopists detected a loss of image quality and a reduction in diagnostic usability with any reduction in the technical specification. Modern methods of data compression, broadband networks and a network protocol with good quality-of-service guarantees are therefore prerequisites for diagnostic rtDVT.

Algorithms↗
PubMed1994

ECG data compression using adaptive Fourier coefficients estimation.

In this article, a new ECG data compression technique is proposed. The method relies on modelling quasi-periodic ECG signals as a dynamic Fourier series. Fourier coefficients are continuously estimated using the adaptive least-mean-square algorithm. Results from simulated normal and pathological ECGs are presented and discussed. A comparison with other compression techniques is also discussed.

Electrocardiography, Ambulatory↗
PubMed2005

Lossy data compression with random gates.

We introduce a new protocol for a lossy data compression algorithm which is based on constraint satisfaction gates. We show that the theoretical capacity of algorithms built from standard parity-check gates converges exponentially fast to the Shannon's bound when the number of variables seen by each gate increases. We then generalize this approach by introducing random gates. They have theoretical performances nearly as good as parity checks, but they offer the great advantage that the encoding can be done in linear time using the survey inspired decimation algorithm, a powerful algorithm for constraint satisfaction problems derived from statistical physics.

Journal Article↗
PubMed1994

Irreversible data compression in chest imaging using computed radiography: an evaluation.

We studied the feasibility of a recently developed 20:1 to 25:1 compression technique for computed radiography (CR) using lung phantom images, and clinical cases were evaluated. We compared one original and two reconstructed CR films of the same image using compression ratios of 10:1 to 15:1, and 20:1 to 25:1, and the deterioration of image quality was carefully checked. Evaluation by subjective ranking of various abnormal opacities created for phantoms and clinical cases showed deterioration of image quality with 20:1 to 25:1 compression images. This was greatest for a single linear opacity (thin line) and least for reticulonodular opacities. Receiver operating characteristics (ROC) for pneumothorax, pulmonary nodules and reticulonodular opacities in newborns, infants, and adults revealed no significant differences among the original 10:1 to 15:1, and 20:1 to 25:1 compression images. We conclude that this new method of data compression of CR images is clinically applicable and useful.

Adult↗
PubMed1997

[Assessing indexes in the ECG data compression].

This paper describes various numerical assessing indexes in evaluating algorithms for the ECG data compression, such as numerical indexes of similarity (CC, RMS, PRD), indexes of error (amplitude error, peak error), efficiency of compression (Compression Ratio, bit rate), computing complexity, and so on. It also involves visual inspection which is more important in assessing algorithms.

Algorithms↗
PubMed2003

Scalable medical data compression and transmission using wavelet transform for telemedicine applications.

In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.

Algorithms↗
PubMed1992

Conservation and characterisation of spatial features in a new method of data compression for body surface potential maps.

Body surface potential maps consist of a huge amount of data represented as a series of three-dimensional maps, which are time consuming to process and expensive to store. In spite of the continuous interest in body surface potential maps, their use has not become common and they are of no practical use in the clinics. This is due to the overwhelming amount of measured data required to generate the maps and the lack of quantitative methods to analyse them. Data compression or reduction may solve these deficiencies. Such a procedure must conserve the fine spatial details of the maps, which are usually extracted from low level surface potentials, as these are reported to be significant in diagnostic electrocardiography. A technique is presented for data reduction, that implements two-level thresholding and conserves the fine significant spatial features of each map. A sequence of annuli thus produced is shown to describe the dynamic nature of the underlying process. This sequence is further processed and characterised by features which quantify its dynamic behaviour: time of annuli sequence appearance, its duration, three-dimensional loci of centres of mass of the annuli, distances between successive centres of mass and cross-correlation coefficients between successive annuli. To test the data reduction procedure and the usefulness of the features, maps from 20 subjects are studied (both normal patients and those with various pathologies). It is found that the use of annuli instead of the whole measured information allows simple storage, display and calculations; the features, which vary in time, represent closely the changes in location of the annuli and their dynamic variations of shape. The features are also found to be grouped together for the maps of the normal patients and for each pathology. Thus, body surface potential maps may become more commonly used in clinics by being represented by a set of features, which conserve their dynamic and spatial nature, and which may serve for classification of cardiac pathologies.

Action Potentials↗
PubMedDate not supplied

ECG data compression using Hebbian neural networks.

Principal component analysis has long been used for a variety of signal processing applications, including signal compression. Neural network implementations of principal component analysis provide a means for unsupervised feature discovery and dimension reduction. In this paper, we describe a method for the compression of ECG data using principal component analysis. Hebbian neural networks were used for principal components computation. A variety of examples of normal and pathological ECGs obtained from the MIT ECG database demonstrate that the proposed method can provide compression ratio up to 30 with PRD% less than 5%.

Algorithms↗
PubMed2001

ECG data compression using wavelets and higher order statistics methods.

This paper evaluates the compression performance and characteristics of two wavelet coding compression schemes of electrocardiogram (ECG) signals suitable for real-time telemedical applications. The two proposed methods, namely the optimal zonal wavelet coding (OZWC) method and the wavelet transform higher order statistics-based coding (WHOSC) method, are used to assess the ECG compression issues. The WHOSC method employs higher order statistics (HOS) and uses multirate processing with the autoregressive HOS model technique to provide increasing robustness to the coding scheme. The OZWC algorithm used is based on the optimal wavelet-based zonal coding method developed for the class of discrete "Lipschitizian" signals. Both methodologies were evaluated using the normalized rms error (NRMSE) and the average compression ratio (CR) and bits per sample criteria, applied on abnormal clinical ECG data samples selected from the MIT-BIH database and the Creighton University Cardiac Center database. Simulation results illustrate that both methods can contribute to and enhance the medical data compression performance suitable for a hybrid mobile telemedical system that integrates these algorithmic approaches for real-time ECG data transmission scenarios with high CRs and low NRMSE ratios, especially in low bandwidth mobile systems.

Algorithms↗
PubMed1993

Multichannel ECG data compression by multirate signal processing and transform domain coding techniques.

In this paper, a multilead ECG data compression method is presented. First, a linear transform is applied to the standard ECG lead signals which are highly correlated with each other. In this way a set of uncorrelated transform domain signals is obtained. Then, resulting transform domain signals are compressed using various coding methods, including multirate signal processing and transform domain coding techniques.

Algorithms↗