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At least 343 records · Page 19Linked to original sources
PubMed2005

Detecting application-level failures in component-based internet services.

Most Internet services (e-commerce, search engines, etc.) suffer faults. Quickly detecting these faults can be the largest bottleneck in improving availability of the system. We present Pinpoint, a methodology for automating fault detection in Internet services by: 1) observing low-level internal structural behaviors of the service; 2) modeling the majority behavior of the system as correct; and 3) detecting anomalies in these behaviors as possible symptoms of failures. Without requiring any a priori application-specific information, Pinpoint correctly detected 89%-96% of major failures in our experiments, as compared with 20%-70% detected by current application-generic techniques.

Algorithms↗
PubMed2005

Development of an automation system for single nucleotide polymorphisms genotyping using bio-strand, a new three-dimensional microarray.

Previously, we developed a novel three-dimensional microarray system called Bio-Strand, which may be used in various applications including single nucleotide polymorphisms genotyping. In Bio-Strand, samples for detection are immobilized on a one-dimensional thread, which is wound around a cylinder-shaped core to form a three-dimensional thread-and-core structure. The thread-and-core structure is then inserted into a plastic pipette tip, where hybridization and detection are performed. In this study, we have developed an automation system, NIAGALA Bio-Station SDx, which enables automated hybridization and detection during the genotyping procedure using Bio-Strand. Using this system, we have performed the single nucleotide polymorphism (SNP) genotyping of CYP2C, one of the important human cytochrome P450 genes and the results were completely consistent with the genotyping results determined by the TaqMan method.

Algorithms↗
PubMed2006

Automated acquisition of electron microscopic random conical tilt sets.

Single particle reconstruction using the random conical tilt data collection geometry is a robust method for the initial determination of macromolecular structures by electron microscopy. Unfortunately, the broad adoption of this powerful approach has been limited by the practical challenges inherent in manual data collection of the required pairs of matching high and low tilt images (typically 60 degrees and 0 degrees). The microscopist is obliged to keep the imaging area centered during tilting as well as to maintain accurate focus in the tilted image while minimizing the overall electron dose, a challenging and time consuming process. To help solve these problems, we have developed an automated system for the rapid acquisition of accurately aligned and focused tilt pairs. The system has been designed to minimize the dose incurred during alignment and focusing, making it useful in both negative stain and cryo-electron microscopy. The system includes a feature for montaging untilted images to ensure that all of the particles in the tilted image may be used in the reconstruction.

Computational Biology↗
PubMed1998

A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms.

We compared the performance of three computer based classification methods when applied to the problem of detecting microaneurysms on digitised angiographic images of the retina. An automated image processing system segmented 'candidate' objects (microaneurysms or spurious objects), and produced a list of features on each candidate for use by the classifiers. We compared an empirically derived rule based system with two automated methods, linear discriminant analysis and a learning vector quantiser artificial neural network, to classify the objects as microaneurysms or otherwise. ROC analysis shows that the rule based system gave a higher performance than the other methods (p = 0.92) although a much greater development time is required.

Aneurysm↗
PubMed2005

Automated melanoma detection with a novel multispectral imaging system: results of a prospective study.

The aim of this research was to evaluate the performance of a new spectroscopic system in the diagnosis of melanoma. This study involves a consecutive series of 1278 patients with 1391 cutaneous pigmented lesions including 184 melanomas. In an attempt to approach the 'real world' of lesion population, a further set of 1022 not excised clinically reassuring lesions was also considered for analysis. Each lesion was imaged in vivo by a multispectral imaging system. The system operates at wavelengths between 483 and 950 nm by acquiring 15 images at equally spaced wavelength intervals. From the images, different lesion descriptors were extracted related to the colour distribution and morphology of the lesions. Data reduction techniques were applied before setting up a neural network classifier designed to perform automated diagnosis. The data set was randomly divided into three sets: train (696 lesions, including 90 melanomas) and verify (348 lesions, including 53 melanomas) for the instruction of a proper neural network, and an independent test set (347 lesions, including 41 melanomas). The neural network was able to discriminate between melanomas and non-melanoma lesions with a sensitivity of 80.4% and a specificity of 75.6% in the 1391 histologized cases data set. No major variations were found in classification scores when train, verify and test subsets were separately evaluated. Following receiver operating characteristic (ROC) analysis, the resulting area under the curve was 0.85. No significant differences were found among areas under train, verify and test set curves, supporting the good network ability to generalize for new cases. In addition, specificity and area under ROC curve increased up to 90% and 0.90, respectively, when the additional set of 1022 lesions without histology was added to the test set. Our data show that performance of an automated system is greatly population dependent, suggesting caution in the comparison with results reported in the literature. In our opinion, scientific reports should provide, at least, the median values of thickness and dimension of melanomas, as well as the number of small (6 mm) melanomas.

Adolescent↗
PubMed2004

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis.

MOTIVATION: Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types. RESULTS: Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets. AVAILABILITY: The software system GEMS is available for download from http://www.gems-system.org for non-commercial use. CONTACT: alexander.statnikov@vanderbilt.edu.

Algorithms↗
PubMed2002

Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction.

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale measures that minimize these effects. Monte Carlo experiments using simulated and clinical data are used to quantify how these "dynamic-scale" enhancements address clinical needs regarding speed, accuracy, and automation. In particular, we show that dynamic-scale ridge traversal is insensitive to its initial parameter settings, operates with little additional computational overhead, tracks centerlines with subvoxel accuracy, passes branch points, and handles significant image noise. We also illustrate the capabilities of the method for medical applications involving a variety of tubular structures in clinical data from different organs, patients, and imaging modalities.

Algorithms↗
PubMed2003

Median-based robust algorithms for tracing neurons from noisy confocal microscope images.

This paper presents a method to exploit rank statistics to improve fully automatic tracing of neurons from noisy digital confocal microscope images. Previously proposed exploratory tracing (vectorization) algorithms work by recursively following the neuronal topology, guided by responses of multiple directional correlation kernels. These algorithms were found to fail when the data was of lower quality (noisier, less contrast, weak signal, or more discontinuous structures). This type of data is commonly encountered in the study of neuronal growth on microfabricated surfaces. We show that by partitioning the correlation kernels in the tracing algorithm into multiple subkernels, and using the median of their responses as the guiding criterion improves the tracing precision from 41% to 89% for low-quality data, with a 5% improvement in recall. Improved handling was observed for artifacts such as discontinuities and/or hollowness of structures. The new algorithms require slightly higher amounts of computation, but are still acceptably fast, typically consuming less than 2 seconds on a personal computer (Pentium III, 500 MHz, 128 MB). They produce labeling for all somas present in the field, and a graph-theoretic representation of all dendritic/axonal structures that can be edited. Topological and size measurements such as area, length, and tortuosity are derived readily. The efficiency, accuracy, and fully-automated nature of the proposed method makes it attractive for large-scale applications such as high-throughput assays in the pharmaceutical industry, and study of neuron growth on nano/micro-fabricated structures. A careful quantitative validation of the proposed algorithms is provided against manually derived tracing, using a performance measure that combines the precision and recall metrics.

Algorithms↗
PubMed2004

An integrative and interactive framework for improving biomedical pattern discovery and visualization.

Recent progress in medical sciences has led to an explosive growth of data. Due to its inherent complexity and diversity, mining such volumes of data to extract relevant knowledge represents an enormous challenge and opportunity. Interactive pattern discovery and visualization systems for biomedical data mining have received relatively little attention. Emphasis has been traditionally placed on automation and supervised classification problems. Based on self-adaptive neural networks and pattern-validation statistical tools, this paper presents a user-friendly platform to support biomedical pattern discovery and visualization. It has been tested on several types of biomedical data, such as dermatology and cardiology data sets. The results indicate that in comparison to traditional techniques, such as Kohonen Maps, this platform may significantly improve the effectiveness and efficiency of pattern discovery and classification tasks, including problems described by several classes. Furthermore, this study shows how the combination of graphical and statistical tools may make these patterns more meaningful.

Algorithms↗
PubMedDate not supplied

Multilevel adaptive process control of acquisition and post-processing of computed radiographic images in picture archiving and communication system environment.

Computed radiography (CR) has become a widely used imaging modality replacing the conventional screen/film procedure in diagnostic radiology. After a latent image is captured in a CR imaging plate, there are seven key processes required before a CR image can be reliably archived and displayed in a picture archiving and communication system (PACS) environment. Human error, computational bottlenecks, software bugs, and CR system errors often crash the CR acquisition and post-processing computers which results in a delay of transmitting CR images for proper viewing at the workstation. In this paper, we present a control theory and a fault tolerance algorithm, as well as their implementation in the PACS environment to circumvent such problems. The software implementation of the control theory and the algorithm is based on the event-driven, multilevel adaptive processing structure. The automated software has been used to provide real-time monitoring and control of CR image acquisition and post-processing in the intensive care unit module of the PACS operation at the University of California, San Francisco. Results demonstrate that the multilevel adaptive process control structure improves CR post-processing time, increases the reliability of the CR images delivery, minimizes user intervention, and speeds up the previously time-consuming quality assurance procedure.

Algorithms↗
PubMed2002

Automatic segmentation of echocardiographic sequences by active appearance motion models.

A novel extension of active appearance models (AAMs) for automated border detection in echocardiographic image sequences is reported. The active appearance motion model (AAMM) technique allows fully automated robust and time-continuous delineation of left ventricular (LV) endocardial contours over the full heart cycle with good results. Nonlinear intensity normalization was developed and employed to accommodate ultrasound-specific intensity distributions. The method was trained and tested on 16-frame phase-normalized transthoracic four-chamber sequences of 129 unselected infarct patients, split randomly into a training set (n = 65) and a test set (n = 64). Borders were compared to expert drawn endocardial contours. On the test set, fully automated AAMM performed well in 97% of the cases (average distance between manual and automatic landmark points was 3.3 mm, comparable to human interobserver variabilities). The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms. The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs.

Algorithms↗
PubMed2003

Optimized region finding and edge detection of knee cartilage surfaces from magnetic resonance images.

Expert hand-drawing of magnetic resonance image (MRI) features can be tedious and time consuming. MRI of the knee were acquired from eight subjects to develop an automated segmentation approach. The regions of interest (ROI) were femur, tibia, and patella cartilage. The Karhunen-Loeve transformation was used to construct prototypical ROI with accentuated features and reduced noise level. Adaptive template matching was then used to translate the prototypical ROI locations for detection and optimal overlap of ROI in test images. Cartilage boundaries at the optimal overlap area were computed based on standard gradient methods.

Cartilage, Articular↗
PubMed2005

TRAP: automated classification, quantification and annotation of tandemly repeated sequences.

TRAP, the Tandem Repeats Analysis Program, is a Perl program that provides a unified set of analyses for the selection, classification, quantification and automated annotation of tandemly repeated sequences. TRAP uses the results of the Tandem Repeats Finder program to perform a global analysis of the satellite content of DNA sequences, permitting researchers to easily assess the tandem repeat content for both individual sequences and whole genomes. The results can be generated in convenient formats such as HTML and comma-separated values. TRAP can also be used to automatically generate annotation data in the format of feature table and GFF files.

Algorithms↗
PubMed2004

EC_oligos: automated and whole-genome primer design for exons within one or between two genomes.

SUMMARY: EC_oligos designs oligonucleotides (oligos) from exons of annotated genomic sequence information. It can automatically and rapidly select oligos that are conserved between two sets of sequence data, and can pair up oligos for use as PCR primers. It can do this on a whole-genome scale and according to user-defined criteria. AVAILABILITY: The source code, executable program and user manual are available at ftp://ftp.ebi.ac.uk/pub/software/dos/EC_oligos/.

Algorithms↗
PubMed1997

Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis.

We describe a method for identification of brain structures from MRI data sets. The bulk of the paper concerns an automatic system for finding the anterior and posterior commissures [(AC) and (PC)] in the midsagittal plane. These landmarks are key for the definition of the Talairach space, commonly used in stereotactic neurosurgery, in the definition of common coordinate systems for the pooling of functional positron emission tomography (PET) images and for neuroanatomy studies. The process works according to a step-by-step procedure: it first analyzes the skull limits. A grey-level histogram is then calculated and allows an automated selection of thresholds. Then, the interhemispheric plane is detected. Following an advanced scene analysis in the midsagittal plane for anatomical structures, the AC and the PC are identified. Experimentally, with a set of 200 patients, the process never failed. Its performances and limits are comparable to that of neuroanatomy experts. Those results are due to a high degree of robustness at each step of the program.

Algorithms↗
PubMedDate not supplied

Noninvasive assessment of cutaneous wound healing using ultrahigh-resolution optical coherence tomography.

Ultrahigh-resolution optical coherence tomography (OCT) was used for noninvasive in vivo evaluation of the wound healing process. Cutaneous wounds were induced by 2.5-mm diameter full-thickness punch biopsies on the dorsal surface of seven mice. OCT imaging was performed to assess the structural characteristics associated with the healing process. The OCT results were compared to corresponding histology. Two automated quantitative analysis routines were implemented to identify the dermal-epidermal junction and segment the OCT images. Hallmarks of cutaneous wound healing such as wound size, epidermal migration, dermal-epidermal junction formation, and differences in wound composition were readily identified on the OCT images. Blister formation was also observed. Preliminary findings suggest OCT is a viable tool to noninvasively monitor wound healing in vivo.

Algorithms↗
PubMed2003

3D structural homology detection via unassigned residual dipolar couplings.

Recognition of a protein's fold provides valuable information about its function. While many sequence-based homology prediction methods exist, an important challenge remains: two highly dissimilar sequences can have similar folds-- how can we detect this rapidly, in the context of structural genomics? High-throughput NMR experiments, coupled with novel algorithms for data analysis, can address this challenge. We report an automated procedure for detecting 3D structural homologies from sparse, unassigned protein NMR data. Our method identifies the 3D structural models in a protein structural database whose geometries best fit the unassigned experimental NMR data. It does not use sequence information and is thus not limited by sequence homology. The method can also be used to confirm or refute structural predictions made by other techniques such as protein threading or sequence homology. The algorithm runs in O(pnk(3)) time, where p is the number of proteins in the database, n is the number of residues in the target protein, and k is the resolution of a rotation search. The method requires only uniform (15)N-labelling of the protein and processes unassigned H(N)-(15)N residual dipolar couplings, which can be acquired in a couple of hours. Our experiments on NMR data from 5 different proteins demonstrate that the method identifies closely related protein folds, despite low-sequence homology between the target protein and the computed model.

Algorithms↗
PubMed1999

Automated morphometric analysis in peripheral neuropathies.

We describe a three-step algorithm for the morphometric analysis of color images of nerve specimens, currently used in the diagnosis of peripheral neuropathies. The algorithm first segments the images by applying a clustering method in the color space. It then identifies and eliminates irrelevant regions and, in the final step, calculates the diagnostic parameters required for clinical analysis. The results obtained on 25 images are reported and compared with corresponding measurements made by neurologists.

Algorithms↗