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PubMed2005

Standardization for image characteristics in telemammography using genetic and nonlinear algorithms.

As the soft copy reading and computer assisted diagnosis (CAD) in mammography become more and more important, the standardization of digital images becomes paramount. Telemammography and telemedicine requires the standardization for image characteristics, such as image resolution, bit-depth and intensity response. Soft copy reading and CAD in mammography are both dependent on the characteristics of the source of the digital data, either direct digital mammography or digitized screen-film mammography. An algorithm developed on images from one database may not perform well as on images from another database (with a different digitization). In this paper, we describe two methods based on a genetic algorithm and a nonlinear algorithm for standardization of digitized and digital mammography. The proposed standardization techniques are based on geometric and intensity transformations that are discovered using a set of calibration images. A set of transformation algorithm is used to search for the best standardization.

Algorithms↗

PubMed2004

Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists.

A version of modified particle swarm optimization (PSO) algorithm has been proposed. The PSO algorithm has been modified to adopt to the discrete combinatorial optimization problem and reduce the probability of sinking into local optima. In the modified PSO algorithm, the velocity represents the probability of element in each particle taking value 1 or 0. The modified discrete PSO algorithm is proposed to select variables in MLR and PLS modeling and to predict antagonism of angiotensin II antagonists. The modified C(p) is employed as fitness function. The results were compared to those obtained by GAs. Experimental results have demonstrated that the modified PSO is a useful tool for variable selection which converges quickly towards the optimal position.

Algorithms↗

PubMed2005

Performance of a seizure warning algorithm based on the dynamics of intracranial EEG.

During the past decade, several studies have demonstrated experimental evidence that temporal lobe seizures are preceded by changes in dynamical properties (both spatial and temporal) of electroencephalograph (EEG) signals. In this study, we evaluate a method, based on chaos theory and global optimization techniques, for detecting pre-seizure states by monitoring the spatio-temporal changes in the dynamics of the EEG signal. The method employs the estimation of the short-term maximum Lyapunov exponent (STL(max)), a measure of the order (chaoticity) of a dynamical system, to quantify the EEG dynamics per electrode site. A global optimization technique is also employed to identify critical electrode sites that are involved in the seizure development. An important practical result of this study was the development of an automated seizure warning system (ASWS). The algorithm was tested in continuous, long-term EEG recordings, 3-14 days in duration, obtained from 10 patients with refractory temporal lobe epilepsy. In this analysis, for each patient, the EEG recordings were divided into training and testing datasets. We used the first portion of the data that contained half of the seizures to train the algorithm, where the algorithm achieved a sensitivity of 76.12% with an overall false prediction rate of 0.17h(-1). With the optimal parameter setting obtained from the training phase, the prediction performance of the algorithm during the testing phase achieved a sensitivity of 68.75% with an overall false prediction rate of 0.15h(-1). The results of this study confirm our previous observations from a smaller number of patients: the development of automated seizure warning devices for diagnostic and therapeutic purposes is feasible and practically useful.

Adult↗

PubMed2005

A review and comparison of classification algorithms for medical decision making.

Within a health care setting, it is often desirable from both clinical and operational perspective to capture the uncertainty and variability amongst a patient population, for example to predict individual patient outcomes, risks or resource needs. Homogeneity brings the benefits of increased certainty in individual patient needs and resource utilisation, thus providing an opportunity for both improved clinical diagnosis and more efficient planning and management of health care resources. A number of classification algorithms are considered and evaluated for their relative performances and practical usefulness on different types of health care datasets. The algorithms are evaluated using four criteria: accuracy, computational time, comprehensibility of the results and ease of use of the algorithm to relatively statistically naive medical users. The research has shown that there is not necessarily a single best classification tool, but instead the best performing algorithm will depend on the features of the dataset to be analysed, with particular emphasis on health care data, which are discussed in the paper.

Algorithms↗

PubMed2006

Development and testing of an algorithm to detect implantable cardioverter-defibrillator lead failure.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) lead failures often present as inappropriate shock therapy. An algorithm that can reliably discriminate between ventricular tachyarrhythmias and noise due to lead failure may prevent patient discomfort and anxiety and avoid device-induced proarrhythmia by preventing inappropriate ICD shocks. OBJECTIVES: The goal of this analysis was to test an ICD tachycardia detection algorithm that differentiates noise due to lead failure from ventricular tachyarrhythmias. METHODS: We tested an algorithm that uses a measure of the ventricular intracardiac electrogram baseline to discriminate the sinus rhythm isoelectric line from the right ventricular coil-can (i.e., far-field) electrogram during oversensing of noise caused by a lead failure. The baseline measure was defined as the product of the sum (mV) and standard deviation (mV) of the voltage samples for a 188-ms window centered on each sensed electrogram. If the minimum baseline measure of the last 12 beats was <0.35 mV-mV, then the detected rhythm was considered noise due to a lead failure. The first ICD-detected episode of lead failure and inappropriate detection from 24 ICD patients with a pace/sense lead failure and all ventricular arrhythmias from 56 ICD patients without a lead failure were selected. The stored data were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. RESULTS: The minimum baseline measure for the 24 lead failure episodes (0.28 +/- 0.34 mV-mV) was smaller than the 135 ventricular tachycardia (40.8 +/- 43.0 mV-mV, P <.0001) and 55 ventricular fibrillation episodes (19.1 +/- 22.8 mV-mV, P <.05). A minimum baseline <0.35 mV-mV threshold had a sensitivity of 83% (20/24) with a 100% (190/190) specificity. CONCLUSION: A baseline measure of the far-field electrogram had a high sensitivity and specificity to detect lead failure noise compared with ventricular tachycardia or fibrillation.

Aged↗

PubMed2005

An accurate electrocardiographic algorithm for differentiation of tremor-induced pseudo-ventricular tachycardia and true ventricular tachycardia.

Tremor-induced electrocardiographic artifacts could be misdiagnosed as ventricular tachycardia (VT). However, there has been no electrocardiographic algorithm effectively differentiating pseudo-VT. In this study, we used 3 electrocardiographic "signs": "Sinus" sign, "Spike" sign, and "Notch" sign, and created an electrocardiographic algorithm. The algorithm was prospectively tested in 98 electrocardiographs (37 tremor-induced pseudo-VT and 61 true VT) Thirty-six out of 37 (97.3%) tremor-induced pseudo-VTs could be accurately diagnosed. In conclusion, this is the first study to systemically analyze the tremor-induced pseudo-VT. Our new electrocardiographic algorithm provides a useful tool for a rapid and accurate diagnosis.

Adult↗

PubMed2006

A computer algorithm for the identification of protein interactions from the spectra of masses (PRISM).

A new algorithm is reported to assist with the identification of protein interaction domains by comparing pairs of MALDI mass spectra recorded for protein digests treated with a binding partner versus an untreated control. Known as PRISM, for protein interactions from the spectra of masses, the algorithm imports m/z versus peak area data directly from a pair of MALDI mass spectra recorded for the control and reaction sample. The algorithm is shown to be able to successfully identify antigenic determinants for protein antigens within mixed protein digests. The algorithm has general utility for the comparative analysis of differences within any two mass spectra of any type and is easily implemented using a simple, intuitive graphical user interface (GUI).

Algorithms↗

PubMed2004

Diagnostic fidelity of the Z-buffer segmentation algorithm: preliminary assessment based on intracranial aneurysm detection.

We have developed an algorithm known as the Z-buffer segmentation (ZBS) algorithm for segmenting vascular structures from 3D MRA images. Previously we evaluated the accuracy of the ZBS algorithm on a voxel level in terms of inclusion and exclusion of vascular and background voxels. In this paper we evaluate the diagnostic fidelity of the ZBS algorithm. By diagnostic fidelity we mean that the data preserves the structural information necessary for diagnostic evaluation. This evaluation is necessary to establish the potential usefulness of the segmentation for improved image display, or whether the segmented data could form the basis of a computerized analysis tool. We assessed diagnostic fidelity by measuring how well human observers could detect aneurysms in the segmented data sets. ZBS segmentation of 30 MRA cases containing 29 aneurysms was performed. Image display used densitometric reprojections with shaded surface highlighting that were generated from the segmented data. Three neuroradiologists independently reviewed the generated ZBS images for aneurysms. The observers had 80% sensitivity (90% for aneurysms larger than 2mm) with 0.13 false positives per image. Good agreement with the gold standard for describing aneurysm size and orientation was shown. These preliminary results suggest that the segmentation has diagnostic fidelity with the original data and may be useful for improved visualization or automated analysis of the vasculature.

Algorithms↗

PubMed2006

Customized ablation algorithm for the treatment of steep central islands after refractive laser surgery.

Steep central island (SCI) formation after photorefractive keratectomy (PRK) and laser in situ keratomileusis (LASIK) represents a major drawback in the visual rehabilitation of patients after refractive laser surgery. Because of the small size of SCIs, current ablation algorithms are unable to properly calculate an ablation pattern for customized retreatment. We present the use of a new ablation algorithm for the treatment of SCIs that occurred after PRK or LASIK surgery. This algorithm uses a smaller zone of approximation and takes into account the spherical shift induced by removal of the SCI. In all 3 eyes treated, best spectacle-corrected visual acuity increased to 20/16 and remained stable at the 1- and 3-month follow-up, with disappearance of the SCI in corneal topography. This new treatment algorithm may be of benefit to patients experiencing visual side effects due to SCI formation after PRK or LASIK surgery.

Algorithms↗

PubMed2004

A new algorithm for RNA secondary structure design.

The function of many RNAs depends crucially on their structure. Therefore, the design of RNA molecules with specific structural properties has many potential applications, e.g. in the context of investigating the function of biological RNAs, of creating new ribozymes, or of designing artificial RNA nanostructures. Here, we present a new algorithm for solving the following RNA secondary structure design problem: given a secondary structure, find an RNA sequence (if any) that is predicted to fold to that structure. Unlike the (pseudoknot-free) secondary structure prediction problem, this problem appears to be hard computationally. Our new algorithm, "RNA Secondary Structure Designer (RNA-SSD)", is based on stochastic local search, a prominent general approach for solving hard combinatorial problems. A thorough empirical evaluation on computationally predicted structures of biological sequences and artificially generated RNA structures as well as on empirically modelled structures from the biological literature shows that RNA-SSD substantially out-performs the best known algorithm for this problem, RNAinverse from the Vienna RNA Package. In particular, the new algorithm is able to solve structures, consistently, for which RNAinverse is unable to find solutions. The RNA-SSD software is publically available under the name of RNA Designer at the RNASoft website (www.rnasoft.ca).

Algorithms↗

PubMed2004

Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations.

In complex systems with many degrees of freedom such as spin glass and biomolecular systems, conventional simulations in canonical ensemble suffer from the quasi-ergodicity problem. A simulation in generalized ensemble performs a random walk in potential energy space and overcomes this difficulty. From only one simulation run, one can obtain canonical ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review the generalized ensemble algorithms. Three well-known methods, namely, multicanonical algorithm (MUCA), simulated tempering (ST), and replica-exchange method (REM), are described first. Both Monte Carlo (MC) and molecular dynamics (MD) versions of the algorithms are given. We then present five new generalized-ensemble algorithms which are extensions of the above methods.

Algorithms↗

PubMed2006

A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging.

A unifying theoretical and algorithmic framework for diffusion tensor estimation is presented. Theoretical connections among the least squares (LS) methods, (linear least squares (LLS), weighted linear least squares (WLLS), nonlinear least squares (NLS) and their constrained counterparts), are established through their respective objective functions, and higher order derivatives of these objective functions, i.e., Hessian matrices. These theoretical connections provide new insights in designing efficient algorithms for NLS and constrained NLS (CNLS) estimation. Here, we propose novel algorithms of full Newton-type for the NLS and CNLS estimations, which are evaluated with Monte Carlo simulations and compared with the commonly used Levenberg-Marquardt method. The proposed methods have a lower percent of relative error in estimating the trace and lower reduced chi2 value than those of the Levenberg-Marquardt method. These results also demonstrate that the accuracy of an estimate, particularly in a nonlinear estimation problem, is greatly affected by the Hessian matrix. In other words, the accuracy of a nonlinear estimation is algorithm-dependent. Further, this study shows that the noise variance in diffusion weighted signals is orientation dependent when signal-to-noise ratio (SNR) is low (<or=5). A new experimental design is, therefore, proposed to properly account for the directional dependence in diffusion weighted signal variance.

Algorithms↗

PubMed2006

Detecting neuronal activity changes using an interspike interval algorithm compared with using visual inspection.

A universally accepted method for efficiently detecting neuronal activity changes (NACs) in neurophysiological studies has not been established. Visual inspection is still considered to be one of the most reliable methods, although it is limited when it is used for analyzing large quantities of data. In this study, an algorithm that considers interspike intervals (ISIs) was developed to define the onset of NACs. Two criteria, involving the mean and the standard deviation (S.D.) of the ISIs during a control period, were used in the ISI algorithm to evaluate the NACs that occurred during a detection period. The first, an ISI decrease of more than 1 S.D. from the mean ISI of the control period, proved to be an effective criterion for qualifying the increased NACs (firing rate increases). The second, an ISI increase greater than 3 S.D.s, efficiently demarcated periods of decreased NACs (firing rate decreases). Statistically significant correlations between the detection of NAC onset times by the ISI algorithm and the detection of those times by visual inspections were observed after offline analyses of recorded neuronal activity. The present results suggest that this ISI algorithm is a reliable and efficient way of defining the onset of NACs.

Action Potentials↗

PubMed2006

Multi-class classification algorithm for optical diagnosis of oral cancer.

We report development of a direct multi-class spectroscopic diagnostic algorithm for discrimination of high-grade cancerous tissue sites from low-grade as well as precancerous and normal squamous tissue sites of human oral cavity. The algorithm was developed making use of the recently formulated theory of total principal component regression (TPCR). The in vivo autofluorescence spectral data acquired from patients screened for neoplasm of oral cavity at the Government Cancer Hospital, Indore, was used to train and validate the algorithm. The diagnostic algorithm based on TPCR was found to provide satisfactory performance in classifying the tissue sites in four different classes - high-grade squamous cell carcinoma, low-grade squamous cell carcinoma, leukoplakia, and normal squamous tissue. The classification accuracy for these four classes was observed to be approximately 94%, 100%, 100% and 91% for the training data set (based on leave-one-out cross-validation), and was approximately 90%, 90%, 85% and 88%, respectively for the corresponding classes for the independent validation data set.

Adult↗

PubMed2006

Efficient implementation of a filtered back-projection algorithm using a voxel-by-voxel approach.

The large amount of image data necessary for high-resolution 3D reconstruction of macromolecular assemblies leads to significant increases in the computational time. One of the most time consuming operations is 3D density map reconstruction, and software optimization can greatly reduce the time required for any given structural study. The majority of algorithms proposed for improving the computational effectiveness of a 3D reconstruction are based on a ray-by-ray projection of each image into the reconstructed volume. In this paper, we propose a novel fast implementation of the "filtered back-projection" algorithm based on a voxel-by-voxel principle. Our version of this implementation has been exhaustively tested using both model and real data. We compared 3D reconstructions obtained by the new approach with results obtained by the filtered Back-Projections algorithm and the Fourier-Bessel algorithm commonly used for reconstructing icosahedral viruses. These computational experiments demonstrate the robustness, reliability, and efficiency of this approach.

Algorithms↗

PubMed2005

Statistical analysis of fractal-based brain tumor detection algorithms.

Fractals are geometric objects that have a noninteger fractal dimension (FD). The FD has been exploited for various biomedical recognition applications such as breast tumor and lung tumor detection. Our previous work shows that the FD is useful in the detection of brain tumors when a reference nontumor image is available. In this work, we extend our previous work by statistically validating the results of FD analysis on a set of 80 real MR and CT images. Our half-image technique requires that the tumor is located in one half of the brain whereas our whole-image technique does not. Furthermore, we alleviate the need for a reference (control) nontumor image to compute the tumor FD, which was necessary in our previous work. We also compare the brain tumor detection performance of our algorithms with other fractal-based algorithms in the literature and statistically validate our results against manually segmented tumor images. We find that the tumor region offers a statistically significant lower FD compared with that of the nontumor area for most of the FD algorithms studied in this work. Thus, our statistical analysis suggests that these FD algorithms may be exploited successfully to determine the possible presence and location of brain tumors in MR and CT images.

Algorithms↗

PubMed2005

The human brain's algorithm for extrapolating motion, and its possible gender-dependence.

Some athletes accomplish feats requiring a timing accuracy 100 times better than their reaction time. This capability implies that the brain can accurately extrapolate the motion of objects. We hypothesize that the brain uses a simple algorithm to accomplish this extrapolation, and that the algorithm is influenced by the subject's gender and recent observations of motion. We describe an experiment in timing the motion of a dot across a computer screen designed to discover the motion extrapolation algorithm. Different types of motion of the dot were studied. The experiment was conducted with 126 college students (two-thirds female), who each performed 1000 trials. By using as many as 126,000 trials, the random noise inherent in individual trials averages out, allowing the underlying algorithm to be revealed. The results show that motion extrapolation is done using the average velocity of a moving object with no correction for changes in velocity during the motion, but with an ad hoc adjustment based on recently observed motions and on gender-males having on average a smaller error than females. A future controlled experiment will be needed to establish whether the observed gender difference is due to the greater experience of males with such related tasks as video games and sports.

Algorithms↗

PubMedDate not supplied

SOM-based algorithms for qualitative variables.

It is well known that the SOM algorithm achieves a clustering of data which can be interpreted as an extension of Principal Component Analysis, because of its topology-preserving property. But the SOM algorithm can only process real-valued data. In previous papers, we have proposed several methods based on the SOM algorithm to analyze categorical data, which is the case in survey data. In this paper, we present these methods in a unified manner. The first one (Kohonen Multiple Correspondence Analysis, KMCA) deals only with the modalities, while the two others (Kohonen Multiple Correspondence Analysis with individuals, KMCA_ind, Kohonen algorithm on DISJonctive table, KDISJ) can take into account the individuals, and the modalities simultaneously.

Algorithms↗