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At least 19 recordsLinked to original sources
PubMed2000

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

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

Algorithms↗
PubMed1997

Detection of significant patterns by compression algorithms: the case of approximate tandem repeats in DNA sequences.

MOTIVATION: Compression algorithms can be used to analyse genetic sequences. A compression algorithm tests a given property on the sequence and uses it to encode the sequence: if the property is true, it reveals some structure of the sequence which can be described briefly, this yields a description of the sequence which is shorter than the sequence of nucleotides given in extenso. The more a sequence is compressed by the algorithm, the more significant is the property for that sequence. RESULTS: We present a compression algorithm that tests the presence of a particular type of dosDNA (defined ordered sequence-DNA): approximate tandem repeats of small motifs (i.e. of lengths < 4). This algorithm has been experimented with on four yeast chromosomes. The presence of approximate tandem repeats seems to be a uniform structural property of yeast chromosomes.

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PubMed2006

Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission.

The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noise.

Algorithms↗
PubMed1992

Fast ECG data compression algorithms suitable for microprocessor systems.

ECG data compression techniques have received extensive attention in ECG analysis. Numerous data compression algorithms for ECG signals have been proposed during the last three decades. We describe two algorithms based on the scan-along polygonal approximation algorithm (SAPA) that are suitable for multichannel ECG data reduction on a microprocessor-based system. One represents a modification of SAPA (MSAPA) which adopts the method of integer division table searching to speed up data reduction; the other (CSAPA) combines MSAPA and TP, a turning-point algorithm, to preserve ST segment signals. Results show that our algorithms achieve a compression ratio of more than 5:1 and a percent rms difference (PRD) to the original signal of less than 3.5%. In addition, the maximum execution time of MSAPA for processing one data point is about 50 microseconds. Moreover, the CSAPA algorithm retains all of the details of the ST segment, which are important in ischaemia diagnosis, by employing the TP algorithm.

Algorithms↗
PubMed2004

[An EEG compression algorithm based on embedded zerotree wavelet (EZW)].

OBJECTIVE: To study algorithm with high speed and high compression ratio for EEG signal. METHOD: A wavelet compression algorithm for ECG based on EZW (embedded zerotree wavelet) coding was presented. RESULT: The result of the experiment showed that the algorithm is simple and fast. It can achieve high compression ratio and fidelity. CONCLUSION: The algorithm with high speed and high compression is very effective.

Algorithms↗
PubMed2002

A comparison of two compression algorithms and the detection of caries.

OBJECTIVES: To assess the effect of two compression algorithms (JPEG and wavelet) on the detection of approximal caries. METHODS: Fifteen bitewing radiographs were generated using 100 posterior teeth mounted in blocks. The images were produced on conventional films (Ektaspeed Plus) and scanned at 300 d.p.i. Digital images were then compressed 9:1 with JPEG and wavelet methods. Nine observers detected the presence and depth of approximal caries recorded on a 5-point confidence scale and a 4-point depth scale from images viewed in random order. Histological examination provided the true depth of the lesions. Data were analysed by means of ANOVA. The null hypothesis was that there is no significant difference between the two compression algorithms and the original uncompressed images. RESULTS: JPEG performed significantly worse than the original and the wavelet algorithm (P<0.001) for the detection of dentinal lesions. However, no significant differences were found for the detection of sound surfaces, enamel lesions, and lesions up to the DEJ between JPEG-compressed images and each of the other two modalities. There was also no significant difference between the wavelet-compressed images and the original for all lesion depths. CONCLUSIONS: At a compression ratio of 9:1, there were no significant differences among the original images, JPEG and wavelet compressed images for the detection of enamel caries. JPEG-compressed images performed inferiorly to the original and wavelet-compressed images for the detection of dentinal lesions. Wavelet compression is a better choice than JPEG at the compression ratio investigated in this study.

Algorithms↗
PubMed2006

An effective and efficient compression algorithm for ECG signals with irregular periods.

This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.

Algorithms↗
PubMed2000

Biological sequence compression algorithms.

Today, more and more DNA sequences are becoming available. The information about DNA sequences are stored in molecular biology databases. The size and importance of these databases will be bigger and bigger in the future, therefore this information must be stored or communicated efficiently. Furthermore, sequence compression can be used to define similarities between biological sequences. The standard compression algorithms such as gzip or compress cannot compress DNA sequences, but only expand them in size. On the other hand, CTW (Context Tree Weighting Method) can compress DNA sequences less than two bits per symbol. These algorithms do not use special structures of biological sequences. Two characteristic structures of DNA sequences are known. One is called palindromes or reverse complements and the other structure is approximate repeats. Several specific algorithms for DNA sequences that use these structures can compress them less than two bits per symbol. In this paper, we improve the CTW so that characteristic structures of DNA sequences are available. Before encoding the next symbol, the algorithm searches an approximate repeat and palindrome using hash and dynamic programming. If there is a palindrome or an approximate repeat with enough length then our algorithm represents it with length and distance. By using this preprocessing, a new program achieves a little higher compression ratio than that of existing DNA-oriented compression algorithms. We also describe new compression algorithm for protein sequences.

Algorithms↗
PubMed1999

A low-complexity intracardiac electrogram compression algorithm.

Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patients morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity.

Algorithms↗
PubMed1998

Radiologist evaluation of a multispectral image compression algorithm for magnetic resonance images.

With the advent of teleradiology and picture archiving and communication systems (PACS), the expense and time required for image transmission and long term image archiving become important. The use of validated image compression algorithms can greatly reduce these costs. A lossy, multispectral image compression scheme at compression ratios (CR) of 25:1 and 32:1 was used for a set of 26 different patient MR exams. The original and compressed/decompressed (CD) images sets were evaluated in a blinded fashion by four radiologists in two phases. The main objective was to determine whether radiologic interpretation would vary between the two types of CD image sets and the corresponding originals. In general, the compression algorithm caused a slight decrease in image quality; however, the interpretation of pathology did not change between the original and CD image sets. In only one case at the maximum CR = 32 did one of four radiologists change the interpretation of pathology after CD. In this study, lossy multispectral image compression of MR images at CR = 25 maintained diagnostic integrity. This could play a significant role in image storage and communications.

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PubMed1996

A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals.

In this paper, a novel family of compression algorithms is presented, which is designed to exploit the redundancy of one-dimensional (1-D) semiperiodical biomedical signals resulting from the cyclic nature of the underlying physical process. The basic idea is that a pool of past-seen cycles is maintained and cycles to be encoded can be stored as transformed versions of those residing in the pool. Conceptually, this approach is an extension of dictionary-based coding schemes used for text compression to signal patterns residing in an n-dimensional space. A cycle transformation method is introduced in order to render the pattern matching process practical and to enable cycle substitution. Based on the principles of the algorithmic family and this transformation method, an electrocardiogram (ECG)-oriented algorithm is implemented and thoroughly tested. The performance of this implementation is examined theoretically and deductions about the optimal algorithm settings are made. The ECG compression algorithm is superior to the average beat subtraction algorithm as proposed by Hamilton and Tompkins in cases where high compression ratios are required.

Algorithms↗
PubMed1999

A Compression Algorithm for DNA Sequences and Its Applications in Genome Comparison.

We present a lossless compression algorithm, GenCompress, for genetic sequences, based on searching for approximate repeats. Our algorithm achieves the best compression ratios for benchmark DNA sequences. Significantly better compression results show that the approximate repeats are one of the main hidden regularities in DNA sequences. We then describe a theory of measuring the relatedness between two DNA sequences. Using our algorithm, we present strong experimental support for this theory, and demonstrate its application in comparing genomes and constructing evolutionary trees.

Journal Article↗
PubMed1988

A data compression algorithm for the electroencephalogram.

This paper describes a data compression algorithm for the EEG using a local error measure. The algorithm discards input signal samples provided they can be reconstructed from the stored samples with an error smaller than a given threshold, and according to simple reconstruction functions (the hold, the ramp, and the cosine). An equivalent (in terms of storage) sampling rate of 44 Hz is achieved without noticeable degradation of the signal quality for visual analysis. The method can be easily implemented for real time multichannel data compression/reconstruction.

Algorithms↗
PubMed2005

[Lossless ECG compression algorithm with anti- electromagnetic interference].

Based on the study of ECG signal features, a new lossless ECG compression algorithm is put forward here. We apply second-order difference operation with anti- electromagnetic interference to original ECG signals and then, compress the result by the escape-based coding model. In spite of serious 50Hz-interference, the algorithm is still capable of obtaining a high compression ratio.

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PubMed1989

Comparison of information-preserving and information-losing data-compression algorithms for CT images.

Data compression increases the number of images that can be stored on magnetic disks or tape and reduces the time required for transmission of images between stations. Two algorithms for data compression are compared in application to computed tomographic (CT) images. The first, an information-preserving algorithm combining differential and Huffman encoding, allows reconstruction of the original image. A second algorithm alters the image in a clinically acceptable manner. This second algorithm combines two processes: the suppression of data outside of the head or body and the combination of differential and Huffman encoding. Because the final image is not an exact copy, the second algorithm is information losing. Application of the information-preserving algorithm can double or triple the number of CT images that can be stored on hard disk or magnetic tape. This algorithm may also double or triple the speed with which images may be transmitted. The information-losing algorithm can increase storage or transmission speed by a factor of five. The computation time on this system is excessive, but dedicated hardware is available to allow efficient implementation.

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PubMed2006

Detecting computer-induced errors in remote-sensing JPEG compression algorithms.

The JPEG image compression standard is very sensitive to errors. Even though it contains error resilience features, it cannot easily cope with induced errors from computer soft faults prevalent in remote-sensing applications. Hence, new fault tolerance detection methods are developed to sense the soft errors in major parts of the system while also protecting data across the boundaries where data flow from one subsystem to the other. The design goal is to guarantee no compressed or decompressed data contain computer-induced errors without detection. Detection methods are expressed at the algorithm level so that a wide range of hardware and software implementation techniques can be covered by the fault tolerance procedures while still maintaining the JPEG output format. The major subsystems to be addressed are the discrete cosine transform, quantizer, entropy coding, and packet assembly. Each error detection method is determined by the data representations within the subsystem or across the boundaries. They vary from real number parities in the DCT to bit-level residue codes in the quantizer, cyclic redundancy check parities for entropy coding, and packet assembly. The simulation results verify detection performances even across boundaries while also examining roundoff noise effects in detecting computer-induced errors in processing steps.

Algorithms↗
PubMedDate not supplied

Assessment of commercial compression algorithms, of the lossy DCT and lossless types, applied to diagnostic digital image files.

The need for diagnostic image compression of the lossy or irreversible type has been declining due to the rapid increase in commercially available formatted hard disk capacity. It is estimated that the latter has increased about three orders of magnitude in the past 14 years while the size of diagnostic image files has, of course, remained constant. During the same period, despite claims for significantly improved performance by vendors, it seems that only small progress has been made in commercial lossless and lossy compression algorithms. There is still no consensus for lossy compression to a level acceptable for diagnosis. This is mostly considered to be around a ratio of 10:1. However, acceptable compression ratios depend heavily on the type of images processed and may be compared with the 3:1 ratio produced by lossless algorithms. This last value was shown to increase to more than 5.5:1 for gamma-camera images when corrected for the noise content of individual bit planes and for the display capabilities of computer monitors. Therefore, any possible benefits of lossy over lossless compression become questionable when the currently available hard disk capacity and network transmission speed are considered against the inevitable loss of information in the lossy type of compression.

Algorithms↗
PubMed1989

Evaluation of a quadtree-based compression algorithm with digitized urograms.

The effect of a quadtree-based data-compression algorithm on the diagnostic yield in digitized radiographs was studied for 100 urograms. Each image was digitized and reviewed at nine decreasing compression ratios ranging from 90:1 to 4.2:1, followed by a review of the uncompressed digital images. Four radiologists independently reviewed the digitized images and the original radiographs and agreed on a reference standard of 201 findings. Sensitivity, measured by the number of findings noted on the compressed digital images, decreased with increasing compression ratios at and above the 11:1 level. No loss of sensitivity was noted with a compression ratio of 4.2:1. Sensitivity decreased more precipitously for calcifications than for soft-tissue masses. Only a minimal loss of sensitivity for bilateral renal function was noted, even with high compression ratios. False-positive rates were unaffected by compression. The authors conclude that quadtree compression ratios of 11:1 and higher may result in loss of sensitivity in clinically relevant findings.

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