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PubMed2001

Evaluation of center-line extraction algorithms in quantitative coronary angiography.

Objective testing of centerline extraction accuracy in quantitative coronary angiography (QCA) algorithms is a very difficult task. Standard tools for this task are not yet available. We present a simulation tool that generates synthetic angiographic images of a single coronary artery with predetermined centerline and diameter function. This simulation tool was used creating a library of images for the objective comparison and evaluation of QCA algorithms. This technique also provides the means for understanding the relationship between the algorithms' performance and limitations and the vessel's geometrical parameters. In this paper, two algorithms are evaluated and the results are presented.

Algorithms↗

PubMed2002

A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data.

In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

Algorithms↗

PubMed2001

The G-algorithm for extraction of robust decision rules--children's postoperative intra-atrial arrhythmia case study.

Clinical medicine is facing a challenge of knowledge discovery from the growing volume of data. In this paper, a data mining algorithm (G-algorithm) is proposed for extraction of robust rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. The G-algorithm is applied to the data set obtained for children born with a malformation of the heart (univentricular heart). As the result of the Fontan surgical procedure, designed to palliate the children, 10%-35% of patients postoperatively develop an arrhythmia known as the intra-atrial reentrant tachycardia. There is an obvious need to identify the children that may develop the tachycardia before the surgery is performed. Prior attempts to identify such children with statistical techniques have been unrewarding. The G-algorithm discussed in this paper shows that there exists an unambiguous relationship between measurable features and the tachycardia. The data set used in this study shows that, for 78.08% of infants, the occurrence of tachycardia can be accurately predicted. The authors' prior computational experience with diverse medical data sets indicates that the percentage of accurate predictions may become even higher if data on additional features is collected for a larger data set.

Algorithms↗

PubMed2005

Efficient algorithms and software for detection of full-length LTR retrotransposons.

LTR retrotransposons constitute one of the most abundant classes of repetitive elements in eukaryotic genomes. In this paper, we present a new algorithm for detection of full-length LTR retrotransposons in genomic sequences. The algorithm identifies regions in a genomic sequence that show structural characteristics of LTR retrotransposons. Three key components distinguish our algorithm from that of current software - (i) a novel method that preprocesses the entire genomic sequence in linear time and produces high quality pairs of LTR candidates in running time that is constant per pair, (ii) a thorough alignment-based evaluation of candidate pairs to ensure high quality prediction, and (iii) a robust parameter set encompassing both structural constraints and quality controls providing users with a high degree of flexibility. Validation of both our serial and parallel implementations of the algorithm against the yeast genome indicates both superior quality and performance results when compared to existing software.

Algorithms↗

PubMed2003

Finite-element time-domain algorithms for modeling linear Debye and Lorentz dielectric dispersions at low frequencies.

We present what we believe to be the first algorithms that use a simple scalar-potential formulation to model linear Debye and Lorentz dielectric dispersions at low frequencies in the context of finite-element time-domain (FETD) numerical solutions of electric potential. The new algorithms, which permit treatment of multiple-pole dielectric relaxations, are based on the auxiliary differential equation method and are unconditionally stable. We validate the algorithms by comparison with the results of a previously reported method based on the Fourier transform. The new algorithms should be useful in calculating the transient response of biological materials subject to impulsive excitation. Potential applications include FETD modeling of electromyography, functional electrical stimulation, defibrillation, and effects of lightning and impulsive electric shock.

Algorithms↗

PubMed2004

On-line segmentation algorithm for continuously monitored data in intensive care units.

An on-line segmentation algorithm is presented in this paper. It is developed to preprocess data describing the patient's state, sampled at high frequencies in intensive care units, with a further purpose of alarm filtering. The algorithm splits the signal monitored into line segments--continuous or discontinuous--of various lengths and determines on-line when a new segment must be calculated. The delay of detection of a new line segment depends on the importance of the change: the more important the change, the quicker the detection. The linear segments are a correct approximation of the structure of the signal. They emphasise steady-states, level changes and trends occurring on the data. The information returned by the algorithm, which is the time at which the segment begins, its ordinate and its slope, is sufficient to completely reconstruct the filtered signal. This makes the algorithm an interesting tool to provide a processed time history record of the monitored variable. It can also be used to extract on-line information on the signal, such as its trend, in the short or long term.

Algorithms↗

PubMed2006

A control algorithm for autonomous optimization of extracellular recordings.

This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed.

Action Potentials↗

PubMed2006

Constrained least squares filtering algorithm for ultrasound image deconvolution.

A new medical ultrasound tissue model is considered in this paper, which incorporates random fluctuations of the tissue response and provides more realistic interpretation of the received pulse-echo ultrasound signal. Using this new model, we propose an algorithm for restoration of the degraded ultrasound image. The proposed deconvolution is a modification of the classical regularization technique which combines Wiener filter and the constrained least squares (LS) algorithm for restoration of the ultrasound image. The performance of the algorithm is evaluated based on both the simulated phantom images and real ultrasound radio frequency (RF) data. The results show that the algorithm can provide improved ultrasound imaging performance in terms of the resolution gain. The deconvolved images visually show better resolved tissue structures and reduce speckle, which are confirmed by a medical expert.

Algorithms↗

PubMed2006

An algorithm for robust and efficient location of T-wave ends in electrocardiograms.

The purpose of this paper is to propose a new algorithm for T-wave end location in electrocardiograms, mainly through the computation of an indicator related to the area covered by the T-wave curve. Based on simple assumptions, essentially on the concavity of the T-wave form, it is formally proved that the maximum of the computed indicator inside each cardiac cycle coincides with the T-wave end. Moreover, the algorithm is robust to acquisition noise, to wave form morphological variations and to baseline wander. It is also computationally very simple: the main computation can be implemented as a simple finite impulse response filter. When evaluated with the PhysioNet QT database in terms of the mean and the standard deviation of the T-wave end location errors, the proposed algorithm outperforms the other algorithms evaluated with the same database, according to the most recent available publications up to our knowledge.

Algorithms↗

PubMedDate not supplied

An O(N2) algorithm for discovering optimal Boolean pattern pairs.

We consider the problem of finding the optimal combination of string patterns, which characterizes a given set of strings that have a numeric attribute value assigned to each string. Pattern combinations are scored based on the correlation between their occurrences in the strings and the numeric attribute values. The aim is to find the combination of patterns which is best with respect to an appropriate scoring function. We present an O(N2) time algorithm for finding the optimal pair of substring patterns combined with Boolean functions, where N is the total length of the sequences. The algorithm looks for all possible Boolean combinations of the patterns, e.g., patterns of the form p and not q, which indicates that the pattern pair is considered to occur in a given string s, if p occurs in s, AND q does NOT occur in s. An efficient implementation using suffix arrays is presented, and we further show that the algorithm can be adapted to find the best k-pattern Boolean combination in O(Nk) time. The algorithm is applied to mRNA sequence data sets of moderate size combined with their turnover rates for the purpose of finding regulatory elements that cooperate, complement, or compete with each other in enhancing and/or silencing mRNA decay.

3' Untranslated Regions↗

PubMedDate not supplied

Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

Abstracting and Indexing↗

PubMed2005

Image change detection algorithms: a systematic survey.

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

Algorithms↗

PubMed2005

Adaptive snakes using the EM algorithm.

Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A new algorithm is proposed in this paper to overcome this difficulty. The algorithm is based on two key ideas. First, edge points are associated in strokes. Second, each stroke is classified as valid (inlier) or invalid (outlier) and a confidence degree is associated to each stroke. The expectation maximization algorithm is used to update the confidence degrees and to estimate the object contour. It is shown that this is equivalent to the use of an adaptive potential function which varies during the optimization process. Valid strokes receive high confidence degrees while confidence degrees of invalid strokes tend to zero during the optimization process. Experimental results are presented to illustrate the performance of the proposed algorithm in the presence of clutter, showing a remarkable robustness.

Algorithms↗

PubMed2006

An image super-resolution algorithm for different error levels per frame.

In this paper, we propose an image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. These inaccurate estimates, along with the additive Gaussian noise in the low-resolution (LR) image sequence, result in different noise level for each frame. In the proposed algorithm, the LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated. A translational motion model is assumed. The convergence property of the proposed algorithm is analyzed in detail. Our experimental results using both real and synthetic data show the effectiveness of the proposed algorithm.

Algorithms↗

PubMed2006

Performance characterization of the dynamic programming obstacle detection algorithm.

A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.

Algorithms↗

PubMed2006

Precompression quality-control algorithm for JPEG 2000.

In this paper, a precompression quality-control algorithm is proposed. It can greatly reduce computational power of the embedded block coding (EBC) and memory requirement to buffer bit streams. By using the propagation property and the randomness property of the EBC algorithm, rate and distortion of coding passes is approximately predicted. Thus, the truncation points are chosen before actual coding by the entropy coder. Therefore, the computational power, which is measured with the number of contexts to be processed, is greatly reduced since most of the computations are skipped. The memory requirement, which is measured with the amount required to buffer bit streams, is also reduced since the skipped contexts do not generate bit streams. Experimental results show that the proposed algorithm reduces the computational power of the EBC by 80% on average at 0.8 bpp compared with the conventional postcompression rate-distortion optimization algorithm. Moreover, the memory requirement is also reduced by 90%. The average PSNR degrades only about 0.1-0.3 dB, on average.

Algorithms↗

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↗

PubMed2006

Combining algorithms in automatic detection of QRS complexes in ECG signals.

QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks and others. Performance is generally good, but each method has situations where it fails. In this paper, we suggest an approach to automatically combine different QRS complex detection algorithms, here the Pan-Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms.

Algorithms↗