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PubMedDate not supplied

[A computer system for the systematization of MR findings in knee joint diseases].

An inexpensive, easy-access computer-based system is proposed, which was developed for the systematization of the clinical series of knee joint disorders studied with Magnetic Resonance Imaging (MRI). The system is based upon the integration of multimedia technology and Data Base Management Systems (DBMS). The hardware configuration for this project included an Apple Macintosh workstation based on a Motorola 68040 microprocessor and a customized application developed by the authors with the 4th Dimension software. The MR images available only on film were digitized off-line with a solid-state Charge Coupled Device (CCD) scanner with back-light cover for transparency. Otherwise, MR images were acquired on-line through an Ethernet-based local area network from the MR unit or from a SparcStation-Advantage Windows workstation connected with the MR unit. Image post-processing was performed with the Adobe PhotoShop software. The system was devoted to the systematization and analysis of a clinical series of 800 MR studies of the knee. A mean of 10 significant MR images were stored for each examination with a standard image compression algorithm--the Joint Photographic Experts Group (JPEG). This permitted us to save the system's storage space and at the same time to preserve image quality for consultation and teaching purposes, not for diagnosis which is made on the backboard or on the MR unit's or Advantage Window's monitor. Finally, MR findings were indexed with a customized check-list specific for knee joint disorders. On the basis of stored and selected information, it was thus possible to carry out a statistical analysis and to make detailed reports which are useful for scientific purposes, such as the preparation of lectures and papers. Moreover, the system was very useful for patients' follow-up and for the preparation of hypermedia teaching applications on knee joint disorders which are available on the Internet at our World-Wide Web server (URL: http://mbox.unipa.it/radpa/radpa ++ +.html). In conclusion, the system is a cost-effective and user-friendly solution for the multipurpose management of radiologic series.

Humans↗
PubMed1996

Compression and genetic sequence analysis.

A novel approach to genetic sequence analysis is presented. This approach, based on compression of algorithms, has been launched simultaneously by Grumbach and Tahi, Milosavljevic and Rivals. To reduce the description of an object, a compression algorithm replaces some regularities in the description by special codes. Thus a compression algorithm can be applied to a sequence in order to study the presence of those regularities all over the sequence. This paper explains this ability, gives examples of compression algorithms already developed and mentions their applications. Finally, the theoretical foundations of the approach are presented in an overview of the algorithmic theory of information.

Algorithms↗
PubMed2003

Resolution of multicomponent NMR signals using wavelet compression and immune algorithm.

A novel algorithm for fast resolution of multicomponent overlapping nuclear magnetic resonance (NMR) spectrum using the wavelet compression and the immune algorithm (IA) is proposed. Taking advantage of the linear property of the wavelet transform (WT), the overlapping NMR spectrum (antigen) can be compressed by WT before it is input into the immune network, the antibodies are also compressed to the same scheme. After extraction of the compressed information of each component, the resolved NMR spectra can be restored by the WT reconstruction. It was proven that the result is almost the same with the result from an IA, but the calculation speed is much faster.

Algorithms↗
PubMed2001

A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error.

This paper describes a hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression. First, the ECG signal is wavelet transformed using four different discrete wavelet transforms (Daubechies, Coiflet, Biorthogonal and Symmlet). All the wavelet transforms are based on dyadic scales and decompose the ECG signals into five detailed levels and one approximation. Then, the wavelet coefficients are linearly predicted, where the error corresponding to the difference between these coefficients and the predicted ones is minimized in order to get the best predictor. In particular, the residuals of the wavelet coefficients are uncorrelated and hence can be represented with fewer bits compared to the original signal. To further increase the compression rate, the residual sequence obtained after linear prediction is coded using a newly developed coding technique. As a result, a compression ratio (Cr) of 20 to 1 is achieved with percentage root-mean square difference (PRD) less than 4%. The algorithm is compared to an alternative compression algorithm based on the direct use of wavelet transforms. Experiments on selected records from the MIT-BIH arrhythmia database reveal that the proposed method is significantly more efficient in compression. The proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels.

Algorithms↗
PubMed2004

A wavelet-packets based algorithm for EEG signal compression.

Transmission of biomedical signals through communication channels is being used increasingly in clinical practice. This technique requires dealing with large volumes of information, and the electroencephalographic (EEG) signal is an example of this situation. In the EEG, various channels are recorded during several hours, resulting in a great demand of storage capacity or channel bandwidth. This situation demands the use of efficient data compression systems. The objective of this work was to develop an efficient algorithm for EEG lossy compression. In this algorithm, the EEG signal is segmented and then decomposed through Wavelet Packets (WP). The WP decomposition coefficients are thresholded and those having absolute values below the threshold are deleted. The remaining coefficients are appropriately quantized and coded using a run-length coding scheme. The compressed EEG signal can be recovered by an inverse process. Extensive experimental tests were made by applying the algorithm to EEG records and measuring the compression rate (CR) and the distortion in signal segments. The WP transform showed a high robustness, allowing a reasonably low distortion after a compression-decompression process, for CR typically in the range 5-8. The algorithm has a relatively low computational cost, making it appropriate for practical applications.

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↗
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↗
PubMed2004

A low computational complexity algorithm for ECG signal compression.

In this work, a filter bank-based algorithm for electrocardiogram (ECG) signals compression is proposed. The new coder consists of three different stages. In the first one--the subband decomposition stage--we compare the performance of a nearly perfect reconstruction (N-PR) cosine-modulated filter bank with the wavelet packet (WP) technique. Both schemes use the same coding algorithm, thus permitting an effective comparison. The target of the comparison is the quality of the reconstructed signal, which must remain within predetermined accuracy limits. We employ the most widely used quality criterion for the compressed ECG: the percentage root-mean-square difference (PRD). It is complemented by means of the maximum amplitude error (MAX). The tests have been done for the 12 principal cardiac leads, and the amount of compression is evaluated by means of the mean number of bits per sample (MBPS) and the compression ratio (CR). The implementation cost for both the filter bank and the WP technique has also been studied. The results show that the N-PR cosine-modulated filter bank method outperforms the WP technique in both quality and efficiency.

Algorithms↗
PubMed2001

A rate distortion optimal ECG coding algorithm.

Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for addressing the problem. In this paper, we present a time domain algorithm based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal in the rate distortion sense. Although the approach is applicable to any type of signal, we focus, in this paper, on the compression of electrocardiogram (ECG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated [1] that exact optimization algorithms outperform these heuristic approaches by a wide margin with respect to reconstruction error. By formulating the compression problem as a graph theory problem, known optimization theory can be applied in order to yield optimal compression. In this paper, we present an algorithm that will guarantee the smallest possible distortion among all methods applying linear interpolation given an upper bound on the available number of bits. Using a varied signal test set, extensive coding experiments are presented. We compare the results from our coding method to traditional time domain ECG compression methods, as well as, to more recently developed frequency domain methods. Evaluation is based both on percentage root-mean-square difference (PRD) performance measure and visual inspection of the reconstructed signals. The results demonstrate that the exact optimization methods have superior performance compared to both traditional ECG compression methods and the frequency domain methods.

Algorithms↗
PubMed2000

Byte structure variable length coding (BS-VLC): a new specific algorithm applied in the compression of trajectories generated by molecular dynamics

Molecular dynamics is a well-known technique very much used in the study of biomolecular systems. The trajectory files produced by molecular dynamics simulations are extensive, and the classical lossless algorithms give poor efficiencies in their compression. In this work, a new specific algorithm, named byte structure variable length coding (BS-VLC), is introduced. Trajectory files, obtained by molecular dynamics applied to trypsin and a trypsin:pancreatic trypsin inhibitor complex, were compressed using four classical lossless algorithms (Huffman, adaptive Huffman, LZW, and LZ77) as well as the BS-VLC algorithm. The results obtained show that BS-VLC nearly triplicates the compression efficiency of the best classical lossless algorithm, preserving a near lossless behavior. Compression efficiencies close to 50% can be obtained with a high degree of precision, and the maximum efficiency possible (75%), within this algorithm, can be performed with good precision.

Journal Article↗
PubMed2006

Compression of EMG signals with wavelet transform and artificial neural networks.

This paper presents a hybrid adaptive algorithm for the compression of surface electromyographic (S-EMG) signals recorded during isometric and/or isotonic contractions. This technique is useful for minimizing data storage and transmission requirements for applications where multiple channels with high bandwidth data are digitized, such as telemedicine applications. The compression algorithm proposed in this work uses a discrete wavelet transform for spectral decomposition and an intelligent dynamic bit allocation scheme implemented by an approach using the Kohonen layer, which improves the bit allocation for sections of the S-EMG with different characteristics. Finally, data and overhead information are packed by entropy coding. The results for the compression of isometric EMG signals showed that this algorithm has a better performance than standard wavelet compression algorithms presented in the literature (presenting a decrease of at least 5% in per cent residual difference (PRD) for the same compression ratio), and a performance that is comparable with the performance of algorithms based on an embedded zero-tree wavelet. For isotonic EMG signals, its performance is better than the performance of the algorithms based on embedded zero-tree wavelets (presenting a decrease in PRD of about 3.6% for the same compression ratios, in the useful compression range).

Algorithms↗
PubMed2002

The effects of irreversible JPEG compression on an automated algorithm for measuring carotid artery intima-media thickness from ultrasound images.

Our ultrasound practice has begun to investigate automated measurements of carotid artery intima-media thickness (IMT) as an indicator of subtle atherosclerosis. Since our clinical ultrasound images are irreversibly compressed, we investigated the effects of this compression on our IMT measurements. We obtained 10 ultrasound images of normal carotid arteries. These were compressed using JPEG to ratios of 5:1, 10:1, 15:1, 20:1, and 30:1. IMT measurements made from all compressed and uncompressed images were compared. For compression ratios ?10:1, IMT deviations between compressed and uncompressed images were ?0.03 mm. Higher than 10:1, the overall IMT deviations were small (0.01 +/- 0.04 mm), although one 25% deviation was measured. Comparison of other parameters yielded similar results. This initial study indicates that compression at 10:1 using baseline JPEG should have little effect on IMT measurements made using the current algorithm, and that compression to 20:1 or 30:1 may be feasible.

Algorithms↗
PubMed1995

Selection of processing algorithms for digital image compression: a rank-order study.

RATIONALE AND OBJECTIVES: We investigated non-receiver operating characteristic (non-ROC) methods for the selection of processing algorithms for digital image compression. METHODS: We performed a multipoint, rank-order study with 20 posteroanterior chest images, each processed using four different algorithms. Seven radiologists reviewed these alongside the digitized noncompressed image. Observers were forced to rank order the similarity and/or difference of the processed images to the nonprocessed image in each case. RESULTS: A two-way analysis of variance of the rankings was statistically significant (p = .025), indicating that one processing scheme yielded images that were clearly perceived as the most similar to the nonprocessed images. The selected processing scheme was not the one that yielded the lowest quantitative difference from the nonprocessed images as measured by root mean square error. CONCLUSION: Non-ROC study designs that are highly sensitive to small differences among similar images can be used to select processing algorithms.

Algorithms↗
PubMed1995

Compression of protein sequence databases.

We have created an algorithm for compressing a PIR database to assist individual researchers and software developers who utilize sequence database information but may not have huge storage space. The resulting compact databank contains compressed PIR information and an interface written in C which allows fast direct access to the stored information without extensive decompression of corresponding files. The databank files as well as the interface C-file can be used on both PC-compatibles and UNIX-based computers without any modifications. The interface supports all standard PIR Request Network queries (i.e. gets databank SEQ number by entry; for a defined databank SEQ number, gets specified information like: name, organism(s), keyword(s), sequence, sequence features with coordinates, etc.). In contrast with PIR Request Network, our package allows us to call PIR-contained information directly from the C programs, even on a personal computer not on a network. Our PIR-derived databank, SAGITTARIUS PIR, was implemented in the form of separate file sets. Each file set contains database information of independent types (i.e. sequences, entry indexes, organisms, etc.). On a particular computer, the available configuration of the PIR information (and storage space) can be easily changed as needed by the user without affecting retrievals of other types of stored information. Due to an original alignment-based algorithm, in the compression of protein sequences themselves, our package out-performs the well-known ZIP file compressor. For PC-compatibles, a dialogue shell is available which supports all standard PIR Request Network queries plus homology searches, alignments, etc.

Algorithms↗
PubMed2006

[Application study of JPEG2000 image compression in nuclear medicine].

To study the usefulness of JPEG2000 compression for nuclear medicine (NM) image, normal and abnormal static images were compressed using a JPEG2000 plug-in. For lossless algorithm, the compressing ratio (CR) was calculated. For lossy algorithm, images were visually analyzed by NM physicians and ROC curves were generated. The area under ROC curve (AUC) was used as the index of image diagnostic quality. Paired sample t tests were performed to compare the AUCs. It was found that the lossless CR was (1.34+/-0.05) : 1. For lossy algorithm, the AUC decreased with the increase of CR. Comparison between the original and the compressed images revealed no significant difference for 10:1 CR but significant difference for bigger CRs. It was concluded that lossless compression has little usefulness for NM image because of very low CR. While lossy compression isused, the diagnostic quality of static NM images is preserved at CRs 50 : 1,40 : 1, 30 : 1,20 : 1 up to 10 : 1. For other types of NM image, CR should be increased or decreased according to their characteristics, especially the level of intrinsic statistical noise of NM image.

Algorithms↗
PubMed2003

[Telemedicine in Ophthalmology: A Useful Tool in Store-and-Forward Counselling?].

Side by side with the progress in computers and imaging, telemedicine has already been used successfully in a large number of specialties of medicine. But high-quality images are not always suitable for transfer via modem because of their file size. The present experimental study was conducted to determine the effects of image compression by the JPEG algorithm on image quality and on the reliability of diagnosis after compression. Digital photographs of the retina were taken and the JPEG compressed images assessed by an experienced ophthalmologist. The present study showed a high reliability of diagnosis and a good agreement between the observations taken at the patient and at the computer. For this reason, JPEG image compression algorithm is suitable for reducing image size for producing high-quality images manageable for transmitting via modem in a store-and-forward teleophthalmological system.

Algorithms↗
PubMed2004

Compressed pattern matching in DNA sequences.

We propose derivative Boyer-Moore (d-BM), a new compressed pattern matching algorithm in DNA sequences. This algorithm is based on the Boyer-Moore method, which is one of the most popular string matching algorithms. In this approach, we compress both DNA sequences and patterns by using two bits to represent each A, T, C, G character. Experiments indicate that this compressed pattern matching algorithm searches long DNA patterns (length > 50) more than 10 times faster than the exact match routine of the software package Agrep, which is known as the fastest pattern matching tool. Moreover, compression of DNA sequences by this method gives a guaranteed space saving of 75%. In part the enhanced speed of the algorithm is due to the increased efficiency of the Boyer-Moore method resulting from an increase in alphabet size from 4 to 256.

Algorithms↗