Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Pattern Recognition, Automated”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources
PubMed2000

Determining protein structure from electron-density maps using pattern matching.

TEXTAL is an automated system for building protein structures from electron-density maps. It uses pattern recognition to select regions in a database of previously determined structures that are similar to regions in a map of unknown structure. Rotation-invariant numerical values, called features, of the electron density are extracted from spherical regions in an unknown map and compared with features extracted around regions in maps generated from a database of known structures. Those regions in the database that match best provide the local coordinates of atoms and these are accumulated to form a model of the unknown structure. Similarity between the regions in the database and an uninterpreted region is determined firstly by evaluating the numerical difference in feature values and secondly by calculating the electron-density correlation coefficient for those regions with similar feature values. TEXTAL has been successful at building protein structures for a wide range of test electron-density maps and can automatically model entire protein structures in a few hours on a workstation. Models built by TEXTAL from test electron-density maps of known protein structures were accurate to within 0.6-0.7 A root-mean-square deviation, assuming prior knowledge of C(alpha) positions. The system represents a new approach to protein structure determination and has the potential to greatly reduce the time required to interpret electron-density maps in order to build accurate protein models.

Algorithms↗
PubMed1995

Speech technology in 2001: new research directions.

Research in speech recognition and synthesis over the past several decades has brought speech technology to a point where it is being used in "real-world" applications. However, despite the progress, the perception remains that the current technology is not flexible enough to allow easy voice communication with machines. The focus of speech research is now on producing systems that are accurate and robust but that do not impose unnecessary constraints on the user. This chapter takes a critical look at the shortcomings of the current speech recognition and synthesis algorithms, discusses the technical challenges facing research, and examines the new directions that research in speech recognition and synthesis must take in order to form the basis of new solutions suitable for supporting a wide range of applications.

Automation↗
PubMed1988

Automated classification of periodontal disease using bitewing radiographs.

The feasibility of applying a prototype, computer-based pattern recognition system to the objective classification of periodontal disease using dental radiographs was tested. Twenty-nine observer-classified bitewing radiographs, representing seven individuals with varying grades of periodontal disease, were selected. The radiographs were digitized using a computer-controlled TV camera. Mathematical features of these radiographs were interactively extracted using a digital image processing system (International Imaging Systems Model 75 and System/575). The features extracted from these radiographs included the brightness levels of cortical and trabecular bone and ratios of bone-loss to linear-crown height. Twenty-eight mathematically defined features (variables) were determined for each radiograph. Stepwise linear discriminant analysis used these features to classify subjects based on the presence and extent of periodontal disease. This pattern recognition system was able to grade periodontal disease in our test series with percentages of correct classifications ranging from 78.8% to 91%. This technology is particularly applicable to the development of morbidity and activity indices for periodontal diseases.

Alveolar Process↗
PubMed1993

Real-time automated computerized detection of venous air emboli in dogs.

OBJECTIVE: My objective was to develop a real-time pattern recognition system to monitor the precordial Doppler and end-tidal CO2 for characteristic changes of venous air emboli. The system also must check the adequacy of the input signals, to allow for unattended operation. The sensitivity of the precordial Doppler monitoring of the resulting system was the focus of this study. METHODS: The computerized system electronically sampled systolic sounds, the amplitude envelope of Doppler pulsations, and, optionally, end-tidal CO2. Features were defined and calculated from the samples, the means and standard deviations of which were also calculated. During real-time test administrations of intravenous air in anesthetized dogs, each new sample was compared with previous statistics and, when parameters changed beyond calculated limits, an alarm was activated. RESULTS: The sensitivity of the on-line system to an intravenous air injection of 0.025 ml/kg was 33%; to 0.05 ml/kg, 73%; to 0.1 ml/kg, 90%; and to 0.2 ml/kg, 100%. A confounding factor, air lodging in the veins, was detected in the smaller injections; when this was corrected, the sensitivity of the system improved beyond these results. CONCLUSION: An on-line, real-time system, developed for continuous observation of precordial Doppler, has a sensitivity comparable to human observers. This system may improve clinical monitoring particularly in situations where the occurrence of a venous air embolism is not a high probability and, therefore, monitoring is not currently used because of its requirement for human observation. Systems such as the one described may allow many more patients to be monitored for this complication.

Algorithms↗
PubMed1992

Rapid and automated characterisation of seed genotype using Micrograd electrophoresis and pattern-matching software.

New precast microgels are described for use in quickly identifying seed of cereal varieties by determining protein composition within an hour. For example, gliadin proteins are extracted from crushed wheat grain, wheatmeal or flour with ethylene glycol (centrifugation not necessary) and 5 microliters extract is applied to a Micrograd gel (3-15% gel gradient) for ten minutes' electrophoresis at 300 volts in sodium lactate buffer (pH 3.1). Alternatively, precast gels are available for SDS gel electrophoresis for examining a different aspect of grain composition as a means of identification. To further expedite identification, software packages have been developed to match the protein pattern for an unknown sample against those of authentic samples, thus to provide quick and definite identity, based on electrophoretic banding, densitometer scan, HPLC profile, multiple antibody reaction or RFLP pattern (PatMatch program). Furthermore, the program WhatWheat offers advice on the best combination of methods to use for a specific task of identification.

Automation↗
PubMed1986

Image processing--principles and its future applications in reconstructive and aesthetic plastic surgery.

Although the great importance of the visual aspect to medical science, and plastic surgery in particular, is beyond doubt, the utilization of image systems for transmission, storage, and processing of images is not at all widespread. The main reason for this phenomenon is that owing to the high complexity of imaging-related equipment, in comparison with other means such as audio equipment (telephone, tape recorders, and so on), more complicated systems are required, including wider broadcasting channels, much larger storage capacities, and higher processing speeds than are available now. Except for extensive research and development activities in the universities, research institutes, and military organizations, the large potential of the artificial visual aspect is not commonly utilized. In the future, efficient utilization of image processing capabilities in the service of plastic surgery will be achieved by the emerging capabilities for many complicated procedures. These include huge image data base processing, automatic detection of pathologic cases by enhancement of details and recognition of patterns, accurate measurements of the changes and distortions in the processed images, prediction of results to allow planning of treatment, simulation, and training on computerized cases. The new capabilities also include visual control of robotic arms for complicated operations, better audio-visual communication between faraway clinics and medical centers via sophisticated teleconferencing systems--which will save traveling expenses and time and will enable the updating of medical information--and many more applications that will be developed when the basic equipment becomes an integrated part of the clinics and plastic surgery departments. Future image processing will enhance the visual aspect in the plastic surgeon's work and will enable the doctor to expand his or her professional capabilities.

Automation↗
PubMed2000

A new method for the automated alignment of dental radiographs for digital subtraction radiography.

OBJECTIVES: To provide a robust and convenient method by which radiographic images can be spatially aligned for digital radiographic subtraction without the use of manually selected reference points. METHODS: An automated method for image alignment is described which begins with the extraction of a large number of edge features (1500 or more pixels) from each of two radiographic images taken of the same anatomical region of a given patient. The features in the first radiograph are paired, pixel by pixel, with those in the second radiograph using a nearest neighbor criterion. The edge features in the first radiograph are aligned with those in the second radiograph by performing an affine transformation that is consistent with the projection geometry for a plantar parallel X-ray beam. Transformation parameters are determined which provide the smallest spatial alignment error between the two sets of equivalent features. These parameters are found by a closed-form analytic solution, thus enabling a computationally efficient implementation. The final transformation is then applied to the entire first image resulting in a close spatial match to the second image. The performance of three dentists using the automatic method was compared with their performance using a manual method of alignment for eight pairs of images. RESULTS: The root mean squared error in image alignment for the automatic method was 14% lower than that with manual alignment. The variability for the automatic method was half that of the maximal method as measured by the residual error. The automatic method was also three times faster than the manual method. CONCLUSIONS: This method could make digital subtraction more accessible to researchers and practising dentists. Batch mode implementation could enable the processing of large volumes of data. Restriction to a region of interest and improved feature extraction could further improve performance.

Algorithms↗
PubMed2002

Automated segmentation of multispectral brain MR images.

This work presents a robust and comprehensive approach for the in vivo automated segmentation and quantitative tissue volume measurement of normal brain composition from multispectral magnetic resonance imaging (MRI) data. Statistical pattern recognition methods based on a finite mixture model are used to partition the intracranial volume into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) spaces. A masking algorithm initially extracts the brain volume from surrounding extrameningeal tissue. Radio frequency (RF) field inhomogeneity effects in the images are then removed using a recursive method that adapts to the intrinsic local tissue contrast. Our technique supports heterogeneous data with multispectral MR images of different contrast and intensity weighting acquired at varying spatial resolution and orientation. The proposed image segmentation methods have been tested using multispectral T1-, proton density-, and T2-weighted MRI data from young and aged non-human primates as well as from human subjects.

Algorithms↗
PubMed2000

ConfMatch: automating electron-density map interpretation by matching conformations.

Building a protein model from the initial three-dimensional electron-density distribution (density map) is an important task in X-ray crystallography. This problem is computationally challenging because proteins are extremely flexible. The algorithm ConfMatch is a global real-space fitting procedure in torsion-angle space. It solves this 'map-interpretation' problem by matching a detailed conformation of the molecule to the density map (conformational matching). This 'best-match' structure is defined as one which maximizes the sum of the density at atom positions. ConfMatch is a practical systematic algorithm based on a branch-and-bound search. The most important idea of ConfMatch is an efficient method for computing accurate bounds. ConfMatch relaxes the conformational matching problem, a problem which can only be solved in exponential time, into one which can be solved in polynomial time. The solution to the relaxed problem is a guaranteed upper bound for the conformational matching problem. In most empirical cases, these bounds are accurate enough to prune the search space dramatically, enabling ConfMatch to solve structures with more than 100 free dihedral angles. Experiments have shown that ConfMatch may be able to automate the interpretation of density maps of small proteins.

Algorithms↗
PubMed2001

Computer-aided diagnosis in chest radiography: a survey.

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

Algorithms↗
PubMed1993

Computerized pattern recognition of EEG artifact.

Automated artifact classification of quantified EEG (QEEG) epochs from 9 males using linear discriminant analysis showed greater than 85% agreement with judges' opinions. These results were replicated (n = 600 epochs for each sample). Testing the entire sample (n = 5800) illustrated reliable eye artifact (94%) but reduced muscle artifact classification (70%) accuracy. Agreement was lowest in the case of more subtle forms of muscle artifact (i.e., low amplitude muscle), however, less than 4% of these were wrongly classified as non-artifact. Improved data collection techniques retaining high frequency energies are anticipated to improve muscle artifact recognition. Results indicate that low levels of artifact contamination would result when only those epochs classified as non-artifact were accepted for inclusion in further analysis.

Blinking↗