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PubMed1997

A user-centred design approach for introducing computer-based process information systems.

There has been an increasing tendency to use computer-based process information systems as the main interface through which operators interact with complex industrial systems. Although the new technology has produced greater hardware reliability and maintainability, the corresponding potential benefits for operability have not always been achieved. Automation has introduced new forms of design and operating errors. One of the major reasons for this problem has been the lack of human factors advice and user participation early in the design process. This paper discusses a user-centred design approach to increase operability and user acceptance of new technologies and working practices. Application of this approach in the context of a chemical plant indicates its promise, but also highlights the difficulties involved in gaining user participation and management commitment.

Chemical Industry↗
PubMedDate not supplied

Acquiring background knowledge for machine learning using function decomposition: a case study in rheumatology.

Domain or background knowledge is often needed in order to solve difficult problems of learning medical diagnostic rules. Earlier experiments have demonstrated the utility of background knowledge when learning rules for early diagnosis of rheumatic diseases. A particular form of background knowledge comprising typical co-occurrences of several groups of attributes was provided by a medical expert. This paper explores the possibility of automating the process of acquiring background knowledge of this kind and studies the utility of such methods in the problem domain of rheumatic diseases. A method based on function decomposition is proposed that identifies typical co-occurrences for a given set of attributes. The method is evaluated by comparing the typical co-occurrences it identifies as well as their contribution to the performance of machine learning algorithms, to the ones provided by a medical expert.

Algorithms↗
PubMed1996

Autonomous design of artificial neural networks by Neurex.

Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design for an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.

Computer Simulation↗
PubMed2006

Large-scale data exploration with the hierarchically growing hyperbolic SOM.

We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that allows for incremental training with an automated adaptation of lattice size to achieve a prescribed quantization error and (ii) an approximate best match search that utilizes the special structure of the hyperbolic lattice to achieve a tremendous speed-up for large map sizes. Using the MNIST and the Reuters-21578 database as benchmark datasets, we show that the H2SOM yields a highly efficient visualization algorithm that combines the virtues of the SOM with extremely rapid training and low quantization and classification errors.

Algorithms↗
PubMed2006

3D ultrasound analysis of carotid plaque volume and surface morphology.

Morphological characterization of carotid plaques has been used for risk stratification and evaluation of response to therapy, evaluation of new risk factors, genetic research, and for quantifying effects of new anti-atherosclerotic therapies. We developed a 3D US system that allows detailed studies of carotid plaques in 3D. Our software includes 3D reconstruction, viewing, manual and semi-automated segmentation of carotid plaques, and surface morphology analysis to be used for quantitative tracking of plaque changes. We evaluated our plaque quantification software by examining plaque volume measurement accuracy, variability, and plaque surface morphology. We used vascular test phantoms to study segmentation accuracy, and used 48 3D US carotid plaques of patients ranging in size from 13.2 mm(3) to 544.0 mm(3) to study plaque volume measurement variability. We compared results from the semi-automated plaque measurements to the results obtained from manual measurements, which were used as the "gold" standard. We developed a surface plaque morphology quantification technique based on the segmented plaque surface curvature and used it to analyze plaques. Accuracy of plaque volume measurements for the simulated plaques ranged from 4.2% to 1.5% for volumes ranging from 68.5 mm(3) to 286 mm(3). The variability study showed that coefficients of variation in the measurement of plaque volume decreased with increasing plaque size for both inter- (90.8-3.9%) and intra-observer (70.2-3.1%) measurements over the plaque sizes measured. Surface morphology analysis showed that 1 mm ulceration could be quantified and monitored for changes over time. The automated plaque quantification approach showed a little higher intra-observer variability than the manual technique, and its performance was better for segmenting the wall than the lumen. Our results indicate that our approach is sensitive tool and can be used in studies of plaque progression and regression as it relates to atherosclerosis treatment effects and can be used effectively in longitudinal studies for direct measurement carotid plaque volume.

Algorithms↗
PubMed2004

Segmentation of the prostate from suprapubic ultrasound images.

We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification.

Algorithms↗
PubMed2004

Parameter-optimized digital holographic microscope for high-resolution living-cell analysis.

A parameter-optimized off-axis setup for digital holographic microscopy is presented for simultaneous, high-resolution, full-field quantitative amplitude and quantitative phase-contrast microscopy and the detection of changes in optical path length in transparent objects, such as undyed living cells. Numerical reconstruction with the described nondiffractive reconstruction method, which suppresses the zero order and the twin image, requires a mathematical model of the phase-difference distribution between the object wave and the reference wave in the hologram plane. Therefore an automated algorithm is explained that determines the parameters of the mathematical model by carrying out the discrete Fresnel transform. Furthermore the relationship between the axial position of the object and the reconstruction distance, which is required for optimization of the lateral resolution of the holographic images, is derived. The lateral and the axial resolutions of the system are discussed and quantified by application to technical objects and to living cells.

Algorithms↗
PubMedDate not supplied

Automated diagnosis of heart disease in patients with heart murmurs: application of a neural network technique.

This study was conducted to test a three-layered artificial neural network analysis of phonocardiogram recordings to diagnose, automatically and objectively, the condition of the heart in patients with heart murmurs. The data were recorded simultaneously in each of 49 patients with a heart murmur through eight microphones attached to the skin surface with adhesive tape, and were analysed by computer. The diagnosis was automated using a three-layered neural network technique. The neural network generated correct answers in over 70% of cases. Furthermore, about 80% of cases of two concurrent diseases were identified correctly. However, ventricular septal defects were incorrectly classified as aortic stenosis or aortic regurgitation, and patent ductus arteriosus was not diagnosed correctly. Accurate diagnoses can frequently be obtained using a neural network, but accuracy can be improved with further data accumulation.

Artificial Intelligence↗
PubMed2006

Isolation and automated ribotyping of Mycobacterium lentiflavum from drinking water distribution system and clinical specimens.

Automated ribotyping as a tool for identifying of nontuberculous mycobacteria was evaluated. We created a database comprising of riboprints of 60 strains, representing 32 species of nontuberculous mycobacteria. It was shown that combined ribopatterns generated after digestion with EcoRI and PvuII were distinguishable between species of both slow-growing and rapid-growing mycobacteria. The findings were in good agreement with the 16S rRNA gene sequencing results, allowing correct identification of Mycobacterium lentiflavum isolated from clinical specimens and from biofilms growing in public water distribution system. The automated ribotyping was powerful in discriminating between M. lentiflavum and closely related species M. simiae and M. palustre. Mycobacterium lentiflavum strains from drinking water biofilms were resistant to two to four antimycobacterial drugs. The drinking water distribution system may, thus, be a source of nontuberculous mycobacteria resistant to multiple drugs.

Biofilms↗
PubMed2004

Gram-staining characterisation of activated sludge filamentous bacteria by automated colour analysis.

An automated image analysis method has been developed for the monitoring of the Gram-staining characteristics of filamentous bacteria in activated sludge. The binary method of pixel classification agreed with manual estimation (level of correlation of 0.9 for Gram-positive bacteria). Its robustness has been assessed by repeatability tests. Population shifts in terms of Gram-staining characteristics have been monitored in laboratory-scale experiments with two feeding schedules using this technique.

Colony Count, Microbial↗
PubMed2006

Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm.

An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences. We describe the Graph Cuts algorithm and compare the results of Graph Cuts segmentations against "gold standard" manual segmentations and segmentations produced by three popular software packages used by neuroimagers: BrainVisa, CLASP, and SurfRelax.

Adult↗
PubMed2006

Statistical strategy for anisotropic adventitia modelling in IVUS.

Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.

Algorithms↗
PubMed2006

Computer analysis of computed tomography scans of the lung: a survey.

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Algorithms↗
PubMed2005

Automatically annotating documents with normalized gene lists.

BACKGROUND: Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms. RESULTS: We compare the results of the two systems, analyze their merits and argue that the classification based system is preferable for many reasons including performance, simplicity and robustness. Our best systems attain a balanced precision and recall in the range of 74%-92%, depending on the organism.

Animals↗
PubMed2005

Quantification of vesicles in differentiating human SH-SY5Y neuroblastoma cells by automated image analysis.

A new automated image analysis method for quantification of fluorescent dots is presented. This method facilitates counting the number of fluorescent puncta in specific locations of individual cells and also enables estimation of the number of cells by detecting the labeled nuclei. The method is here used for counting the AM1-43 labeled fluorescent puncta in human SH-SY5Y neuroblastoma cells induced to differentiate with all-trans retinoic acid (RA), and further stimulated with high potassium (K+) containing solution. The automated quantification results correlate well with the results obtained manually through visual inspection. The manual method has the disadvantage of being slow, labor-intensive, and subjective, and the results may not be reproducible even in the intra-observer case. The automated method, however, has the advantage of allowing fast quantification with explicitly defined methods, with no user intervention. This ensures objectivity of the quantification. In addition to the number of fluorescent dots, further development of the method allows its use for quantification of several other parameters, such as intensity, size, and shape of the puncta, that are difficult to quantify manually.

Algorithms↗
PubMed2004

On missing Data Treatment for degraded video and film archives: a survey and a new Bayesian approach.

Image sequence restoration has been steadily gaining in importance with the increasing prevalence of visual digital media. The demand for content increases the pressure on archives to automate their restoration activities for preservation of the cultural heritage that they hold. There are many defects that affect archived visual material and one central issue is that of Dirt and Sparkle, or "Blotches." Research in archive restoration has been conducted for more than a decade and this paper places that material in context to highlight the advances made during that time. The paper also presents a new and simpler Bayesian framework that achieves joint processing of noise, missing data, and occlusion.

Algorithms↗
PubMed2005

WaveRead: automatic measurement of relative gene expression levels from microarrays using wavelet analysis.

Gene expression microarrays monitor the expression levels of thousands of genes in an experiment simultaneously. To utilize the information generated, each of the thousands of spots on a microarray image must be properly quantified, including background correction. Most present methods require manual alignment of grids to the image data, and still often require additional minor adjustments on a spot by spot basis to correct for spotting irregularities. Such intervention is time consuming and also introduces inconsistency in the handling of data. A fully automatic, tested system would increase throughput and reliability in this field. In this paper, we describe WaveRead, a fully automated, standalone, open-source system for quantifying gene expression array images. Through the use of wavelet analysis to identify the spot locations and diameters, the system is able to automatically grid the image and quantify signal intensities and background corrections without any user intervention. The ability of WaveRead to perform proper quantification is demonstrated by analysis of both simulated images containing spots with donut shapes, elliptical shapes, and Gaussian intensity distributions, as well as of standard images from the National Cancer Institute.

Algorithms↗
PubMed2004

BCI Competition 2003--Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram.

The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.

Algorithms↗