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PubMed1992

ECG data compression by modeling.

This paper presents a novel algorithm for data compression of single lead Electrocardiogram (ECG) data. The method is based on Parametric modeling of the Discrete Cosine Transformed ECG signal. Improved high frequency reconstruction is achieved by separately modeling the low and the high frequency regions of the transformed signal. Differential Pulse Code Modulation is applied on the model parameters to obtain a further increase in the compression. Compression ratios up to 1:40 were achieved without significant distortion.

Algorithms↗
PubMed2026

FFC: a scalable FASTA compressor.

SUMMARY: FASTA is a widely used text-based format for storing nucleotide and protein sequences. The existing FASTA compressors usually focus on (slightly) improving the compression ratio, not on practical performance. We present FFC, a scalable FASTA compressor that achieves average compression speeds 4.7× and 11.4× higher than two high-performance compressors, zstd and NAF, respectively, across a benchmark set of seven single genomes. It also delivers average decompression speeds 3.5× and 2.7× higher than zstd and NAF, respectively. Although a chunk-based zstd variant with parallel decompression, pzstd, almost matches FFC speed, its compression ratio is on average by 23% worse than FFC's. For the experiment, a 14-core workstation and a RAM disk (to reduce the impact of I/O) were used. AVAILABILITY AND IMPLEMENTATION: FFC is freely available at github.com/kowallus/ffc and also as a Zenodo repository at 10.5281/zenodo.18892353, and the used datasets at 10.5281/zenodo.18873744.

Data Compression↗
PubMed1992

An efficient algorithm for MR image reconstruction and compression.

In magnetic resonance imaging (MRI), the original data are sampled in the spatial frequency domain. The sampled data thus constitute a set of discrete Fourier transform (DFT) coefficients. The image is usually reconstructed by taking inverse DFT. The image data may then be efficiently compressed using the discrete cosine transform (DCT). We present here a method of using DCT to treat the sampled data, which combines two procedures, image reconstruction and data compression. This method may be particularly useful in medical picture archiving and communication systems (PACS) where both image reconstruction and compression are important issues.

Algorithms↗
PubMed1992

Wavelet transform as a potential tool for ECG analysis and compression.

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Electrocardiography↗
PubMed1991

Relation of mean pixel intensity to concentration of sonicated albumin microspheres: effects of ultrasound system settings.

Myocardial contrast echocardiography (MCE) is a recently developed technique to assess myocardial perfusion. Previous studies have shown that MCE frequently underestimates coronary reserve as compared with other methods. Previous reports have suggested that some of the limitations of MCE are due to the nonlinear processing algorithms of the ultrasound systems. Therefore we designed an in vitro model to study the effects of various processing algorithms and levels of acoustic power (AP) on the relation between concentration of echocontrast and mean pixel intensities. A beaker containing a commercially produced, diluted solution of sonicated albumin microspheres was imaged with a commercial Hewlett Packard ultrasound system, and mean pixel intensities were determined with an off-line computer system. The solution was imaged at a wide range of APs (40-10 dB) and time gain compensation (40-60 dB). Concentration versus intensity curves were generated using all possible combinations of compression and processing algorithms available on the ultrasound system used. Contrast effect diminished rapidly when exposed to high AP. Lower powers had less effect on contrast intensity duration, with no effect seen below 20 dB. Changes in time gain compensation did not affect contrast intensities. Regardless of the processing algorithm examined, the relation between concentration of echocontrast and mean pixel intensities was near linear in the first two to three concentrations; thereafter, the curves flattened during increasing concentrations of contrast. At higher concentrations, acoustic shadowing produced a decrease in pixel intensities.(ABSTRACT TRUNCATED AT 250 WORDS)

Acoustics↗
PubMed1987

Image compression for medical imaging systems.

The physical size of typical digital images, in terms of the number of bytes of data one image contains, is large; e.g., a 1024 X 1024 image with 8 bits of data per pixel contains a megabyte of data. To transmit many such images over a network, sometimes over low-capacity phone lines to remote sites, or to store large numbers of images over a long period of time as part of the medical records for patients, the need for image compression arises to alleviate these large demands for image data storage and transmission capacity. This paper discusses image compression in terms of the information theory upon which it is based. The two basic categories of algorithms for implementing image compression are presented along with considerations for image quality and accuracy, which are of primary importance to the medical imaging community.

Diagnostic Imaging↗
PubMed2026

RLBWT-based LCP computation in compressed space for terabase-scale pangenome analysis.

MOTIVATION: Lossless full text indexes are utilized in a myriad of applications in bioinformatics. The continuously decreasing cost of generating biological data has resulted in the need to build full text indexes on biological datasets of increasing size. Many compressed full text indexes have been developed to address this problem. In particular, run-length Burrows-Wheeler transform (RLBWT) based compressed full text indexes have seen wide development and adoption. However, the construction of these RLBWT-based compressed full text indexes is still computationally expensive, sometimes prohibitively so, even for current dataset sizes. RESULTS: Therefore, we present algorithms for the construction of RLBWT-based compressed full text indexes and their supporting data structures in compressed space. The algorithms have a space complexity of O(r) words and run in O(n) time for repetitive datasets, where r is the number of runs in the BWT, n is the length of the text, and repetitive datasets implies nr∈Ω(log n). We provide the first algorithm to compute LCP-related information for repetitive datasets in optimal time and O(r) space, greatly reducing memory requirements. The key idea behind this algorithm is the utilization of r samples of the inverse suffix array at regular intervals. For example, on the Human Pangenome Reference Consortium Release 2 dataset, this reduces peak memory from 2135 GiB to 170 GiB (12.6x reduction) compared to the previous best method (pfp-thresholds). AVAILABILITY AND IMPLEMENTATION: The implementation is available at https://github.com/ucfcbb/TeraTools.

Algorithms↗
PubMed1991

Automated feature detection in digital images of skin.

This paper presents a new approach to the detection of cutaneous features such as pigmented moles, pores and hair in digital images. We show that by considering the digitized image to be a 3-D terrain with brightness being height, common skin features appear as 'pits' in the terrain. Pits contain a great deal of information about local features in a form that can be easily extracted and analyzed. Pigmented lesions of clinical interest typically have pit characteristics which can be used to separate them from other features. We show empirically that, by creating a statistical database, on the average over 99% of pits can be classified correctly after only a few training images are established for a subject. When attempting to detect pigmented lesions, mean sensitivity ranges from 78% to 98%, depending on imaging conditions and the classification algorithm used. Using image compression, the speed of this screening technique is shown to be increased by a factor of 4 without loss of sensitivity.

Algorithms↗
PubMed1990

Standardized evaluation of techniques for measuring the spectral compression of the myoelectric signal.

A digital algorithm was designed to produce band-limited noise with adjustable median frequency and amplitude. This algorithm produces test signals with spectral characteristics typical of those of the surface myoelectric signals encountered in muscle fatigue studies. These synthesized signals provide the basis for standardized evaluation of the performance of various techniques which monitor the spectral compression of the myoelectric signal during muscle fatigue.

Algorithms↗
PubMed1985

A microcomputer program for comparison and alignment of DNA sequence gel readings.

During the course of determining the sequence of a large DNA fragment, it is necessary to cross-check numerous gel readings from different DNA fragments, in order to track and eliminate mistakes. An algorithm is presented that takes advantage of the high degree of homology between such sequences to construct an alignment of the matching regions. It does not require knowledge of a starting homology zone, neither large memory areas, even for sequences of several kilobases, and it can overcome large gaps or mismatch zones that correspond for instance to misinterpretation of compressions on sequence gels. This algorithm has been implemented in 6502 assembly language on an Apple II computer as an extension to the PEGASE sequence handling system.

Algorithms↗
PubMed1987

Back pain in the cancer patient: an algorithm for evaluation and management.

Epidural spinal cord compression is common in patients with metastatic cancer. Back pain is usually the first symptom and may be present for months before neurologic abnormalities occur. A favorable outcome depends on early diagnosis and treatment. For the management of this problem, we propose an algorithm that begins with the treatment of patients who need emergency care and proceeds with an orderly approach to the evaluation of less urgent cases. The central elements include the criteria for myelography and the rational use of corticosteroids, radiation therapy, and surgery.

Algorithms↗
PubMed1985

Is compression occurring in DRG prices?

Many analysts believe that the prices of truly high cost DRGs are set low relative to their actual costs whereas the prices of truly low cost DRGs are set high relative to their costs. To test whether such "compression" is occurring, I examined the algorithm that the Health Care Financing Administration used to create its DRG pricing structure. I then compared the HCFA algorithm with the more refined weighting systems used by Maryland and New Jersey to create their DRG payment systems. I found that compression is indeed occurring in the Medicare system relative to both the New Jersey and the Maryland systems. This compression will assume special urgency for some hospitals when the standardized national DRG payment system is phased in or if stringent limits are placed on the overall rate of increase in DRGs.

Centers for Medicare and Medicaid Services, U.S.↗
PubMed1990

Enhancement and compression of digital chest radiographs.

The application of digital technologies to chest radiography holds the promise of routine application of image processing techniques to effect image enhancement. Because of their inherent spatial resolution, however, digital chest images impose severe constraints on data storage devices. Compression of these images will relax such constraints and facilitate image transmission on a digital network. We evaluated an algorithm for enhancing digital chest images that has allowed significant data compression while improving the diagnostic quality of the image. This algorithm is based on the photographic technique of unsharp masking. Image quality was measured with respect to the task of tumor detection and compression ratios as high as 2:1 were achieved. This compression can be supplemented by irreversible methods.

Algorithms↗
PubMed1989

Needle localization of nonpalpable breast lesions with a portable dual-grid compression system. Work in progress.

A new device and a geometric algorithm facilitate consistent insertion of a needle tip into nonpalpable breast lesions on a single attempt. With the breast compressed by dual grid plates, two oblique exposures are taken. Three intersecting planes through the lesion referenced to the grid markings permit easy determination of the lesion coordinates. In initial trials for preoperative breast lesion localization in 11 patients, the needle tip was within the lesion in nine patients and was within 0.5 cm of the lesion in two patients. The results indicate that this system is very useful for accurate localization and needle aspiration of breast lesions.

Adult↗
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↗
PubMedDate not supplied

Data predictability for compression of digital fluorography images.

Images obtained by digital fluorography were checked for compressability. These images include images of coronary vessels and images of peripheral vessels. These images have a very low signal-to-noise ratio compared to the optical images usually used for developing compression methods. Configurational entropy was used to represent the information content of these images. Reversible prediction algorithms were extensively checked in a search for minimal residual information, enabling more efficient reversible compression. Optimal results were obtained for algorithms based on two or three neighboring pixels and a semiempirical rule, based on the noise level, was found which decides on the best approach. It was found that raw data images are more predictable than subtracted images although the latter are visually preferred.

Algorithms↗
PubMed1986

Toward computerized morphometric facilities: a review of 58 software packages for computer-aided three-dimensional reconstruction, quantification, and picture generation from parallel serial sections.

This review gives an inventory of 58 computer-aided three-dimensional reconstruction applications in the domain of biomedical research. It is devoted to the formulation of a set of recommendations thought to be necessary for improved performance of software packages in this field. These recommendations can be used to select packages and to guide future developments of existing reconstruction systems. The survey is restricted to three-dimensional reconstructions based upon a series of parallel sections of an object. Subjects treated are programming languages, resolution and sampling, input preparation, realignment, local deformation of slices, numerical quantifications, topological complexity, internal representation, display complexity (hidden surfaces, shading, smoothing), structure extraction, descriptive elements, database, data compression, time efficiency of systems and algorithms, hardware configuration, input devices, input media, interactive aids, display devices, and output devices. Information for this survey comes from articles that appeared between 1965 and 1985.

Computers↗
PubMedDate not supplied

Area weighted convolutional interpolation for data reprojection in single photon emission computed tomography.

A data reprojection algorithm has been developed for use in single photon emission computed tomography on an array processor equipped computer system. The algorithm makes use of an accurate representation of pixel activity (uniform square pixel model of intensity distribution), and is rapidly performed due to the efficient handling of an array-based algorithm and the fast Fourier transform on parallel processing hardware. The algorithm consists of using a pixel driven nearest-neighbor projection operation to an array of subdivided projection bins. The subdivided project bin array is then convolved with the angle-dependent projection of the area of a uniform square pixel and compressed to original bin size. The new algorithm has thus been named the area weighted convolution (AWC) method of interpolation. When compared to nearest-neighbor and linear interpolation algorithms, the new AWC algorithm was found to be more accurate, having an accuracy approaching that of the line length algorithm. It also yielded an easier and more efficient implementation on parallel hardware than line length or linear interpolation, with faster execution times than either.

Biometry↗