PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “computer vision”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 55 records · Page 3Linked to original sources
PubMed1999

Automated registration of multimodal brain image sets using computer vision methods.

We present a new method of registering three dimensional image volumes of the brain of a given patient acquired at different times or with different imaging modalities, or both. Registration is an essential requirement for fusing the data from the two image sets so as to either increase the available information by exploiting complementary imaging modalities, or to measure small changes over time for prognostication, disease assessment, etc. The new technique exploits an external, removable, remountable reference frame which is attached to the head. Computer vision techniques are used to determine the positions of fiducial marks in every image. The transformation required to map each image of one image volume onto the other image volume is developed using the theory of quaternions. The results indicate that the new technique is robust and practical in a clinical setting.

Algorithms↗
PubMed1993

A computer vision based technique for 3-D sequence-independent structural comparison of proteins.

A detailed description of an efficient approach to comparison of protein structures is presented. Given the 3-D coordinate data of the structures to be compared, the system automatically identifies every region of structural similarity between the structures without prior knowledge of an initial alignment. The method uses the geometric hashing technique which was originally developed for model-based object recognition problems in the area of computer vision. It exploits a rotationally and translationally invariant representation of rigid objects, resulting in a highly efficient, fully automated tool. The method is independent of the amino acid sequence and, thus, insensitive to insertions, deletions and displacements of equivalent substructures between the molecules being compared. The method described here is general, identifies 'real' 3-D substructures and is not constrained by the order imposed by the primary chain of the amino acids. Typical structure comparison problems are examined and the results of the new method are compared with the published results from previous methods. These results, obtained without using the sequence order of the chains, confirm published structural analogies that use sequence-dependent techniques. Our results also extend previous analogies by detecting geometrically equivalent out-of-sequential-order structural elements which cannot be obtained by current techniques.

Algorithms↗
PubMed1994

Computer vision approaches for the three-dimensional reconstruction of coronary arteries: review and prospects.

The objective of this article is to define the different stages involved in the 3D reconstruction of arteries and to review, from our experience and from the literature, the solutions already proposed. A full reconstruction framework includes the characterization of the imaging device (in terms of distortion and calibration), the specificity of the image acquisition process, the preprocessing that can be applied, the detection of the vascular structures, the 2D feature formation, the reconstruction itself, and the visualization aspects. They are examined according to a computer vision approach where two or three views are assumed to be available. Their generalization to temporal image sequences are also considered. Some of the material reported here is unpublished. The article allows the reader to identify the true critical issues that are not often clearly mentioned in the literature and the challenges that they convey. A final discussion presents a few perspectives in this area of research.

Artificial Intelligence↗
PubMed2004

Robust adaptive-scale parametric model estimation for computer vision.

Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator for the scale of the noise in the (inlier) data. Indeed, we propose two novel robust techniques: the Two-Step Scale estimator (TSSE) and the Adaptive Scale Sample Consensus (ASSC) estimator. TSSE applies nonparametric density estimation and density gradient estimation techniques, to robustly estimate the scale of the inliers. The ASSC estimator combines Random Sample Consensus (RANSAC) and TSSE: using a modified objective function that depends upon both the number of inliers and the corresponding scale. ASSC is very robust to discontinuous signals and data with multiple structures, being able to tolerate more than 80 percent outliers. The main advantage of ASSC over RANSAC is that prior knowledge about the scale of inliers is not needed. ASSC can simultaneously estimate the parameters of a model and the scale of the inliers belonging to that model. Experiments on synthetic data show that ASSC has better robustness to heavily corrupted data than Least Median Squares (LMedS), Residual Consensus (RESC), and Adaptive Least Kth order Squares (ALKS). We also apply ASSC to two fundamental computer vision tasks: range image segmentation and robust fundamental matrix estimation. Experiments show very promising results.

Algorithms↗
PubMed1995

An automated computer vision and robotics-based technique for 3-D flexible biomolecular docking and matching.

The generation of binding modes between two molecules, also known as molecular docking, is a key problem in rational drug design and biomolecular recognition. Docking a ligand, e.g., a drug molecule or a protein molecule, to a protein receptor, involves recognition of molecular surfaces as molecules interact at their surface. Recent studies report that the activity of many molecules induces conformational transitions by 'hinge-bending', which involves movements of relatively rigid parts with respect to each other. In ligand-receptor binding, relative rotational movements of molecular substructures about their common hinges have been observed. For automatically predicting flexible molecular interactions, we adapt a new technique developed in Computer Vision and Robotics for the efficient recognition of partially occluded articulated objects. These type of objects consist of rigid parts which are connected by rotary joints (hinges). Our approach is based on an extension and generalization of the Geometric Hashing and Generalized Hough Transform paradigm for rigid object recognition. Unlike other techniques which match each part individually, our approach exploits forcefully and efficiently enough the fact that the different rigid parts do belong to the same flexible molecule. We show experimental results obtained by an implementation of the algorithm for rigid and flexible docking. While the 'correct', crystal-bound complex is obtained with a small RMSD, additional, predictive 'high scoring' binding modes are generated as well. The diverse applications and implications of this general, powerful tool are discussed.

Algorithms↗
PubMed2006

Computer vision based analysis of potato chips--a tool for rapid detection of acrylamide level.

In this study, analysis of digital color images of fried potato chips were combined with parallel LC-MS based analysis of acrylamide in order to develop a rapid tool for the estimation of acrylamide during processing. Pixels of the fried potato image were classified into three sets based on their Euclidian distances to the representative mean values of typical bright yellow, yellowish brown, and dark brown regions using a semiautomatic segmentation algorithm. The featuring parameter extracted from the segmented image was NA2 value which was defined as the number of pixels in Set-2 divided by the total number of pixels of the entire fried potato image. Using training images of potato chips, it was shown that there was a strong linear correlation (r = 0.989) between acrylamide level and NA2 value. Images of a number of test samples were analyzed to predict their acrylamide level by means of this correlation data. The results confirmed that computer vision system described here provided explicit and meaningful description from the viewpoint of inspection and evaluation purpose for potato chips. Assuming a provisional threshold limit of 1000 ng/g for acrylamide, test samples could be successfully inspected with only one failure out of 60 potato chips.

Acrylamide↗
PubMed2005

Computer vision analysis of collagen fiber bundles in the adventitia of human blood vessels.

Numerical simulations, which are based on reliable biomechanical models of blood vessels, can help to get a better understanding of cardiovascular diseases such as atherosclerosis, and can be used to develop optimal medical treatment strategies.The adventitia is the outer most layer of blood vessels and its mechanical properties are essentially determined by the three-dimensional, structural arrangement of collagen fibre bundles embedded in the tissue. Global information such as the orientation statistics of the fibre bundles as well as detailed information as the crimp of the single fibres within the bundles is of particular interest in biomechanical modeling.In order to obtain a sufficiently large amount of data for biomechanical modeling, a fully automatic method for the structural analysis of the soft tissue is required. In this contribution we present methods based on computer vision to fulfill this task. We start by discussing proper tissue preparation and imaging techniques that have to be used to obtain data, which reliably represents the real three-dimensional tissue structure. The next step is concerned with algorithms that robustly segment the collagen fibre bundles and cope with various kinds of artifacts. Novel segmentation techniques for robust segmentation of individual fibril bundles and methods for estimation of their parameters, such as location, shape, mean fibril orientation, crimp of fibrils, etc, is discussed. The proposed algorithms are based on novel perceptual grouping methods operating on the extracted orientation data of fibrils.Finally, we demonstrate the results obtained by our fully automatic method on real data. In addition, for a more quantitative assessment, we introduce a generative structural model that enables the synthesis of three-dimensional fibre bundles with well-defined characteristics.

Adventitia↗
PubMed1993

Optimisation of a computer vision system for the interpretation of American Sign Language.

Presented in this paper is a simulation algorithm for the optimisation of camera position with respect to the signer, to have a full and reliable interpretation of the American Sign Language. The simulation includes a three-dimensional world point into two-dimensional image point transformation algorithm, the effect of the depth information loss and a sign projection correction test. It is concluded that the viewing camera should be positioned at any point in a specified area subtended by a solid angle of 30 degrees, where the centre of the area is located at 45 degrees in the azimuth and 45 degrees in elevation relative to the signer. The theory and the technique are tested with regard to the efficiency of interpreting American Sign Language (ASL) by two adult signers. One of the signers had been using ASL on a regular basis since infancy, and the second signer had signed for the past five years. It is demonstrated that positioning the camera anywhere in the specified area provides a 96 per cent correct interpretation of the 36 signs tested. The results also provide a preliminary indication that signer variability may not present a major problem in interpretation, and that a computer vision system which captures the optimum depth information can distinguish between signs which, to the naked eye, appear to have similar characteristics.

Algorithms↗
PubMed2006

Computational vision systems for the detection of malignant melanoma.

In recent years, computational vision-based diagnostic systems for dermatology have demonstrated significant progress. We review these systems by first presenting the installation, visual features utilized for skin lesion classification and the methods for defining them. We also describe how to extract these features through digital image processing methods, i.e. segmentation, registration, border detection, color and texture processing, and present how to use the extracted features for skin lesion classification by employing artificial intelligence methods, i.e. discriminant analysis, neural networks, and support vector machines. Finally, we compare these techniques in discriminating malignant melanoma tumors versus dysplastic naevi lesions.

Automation↗
PubMedDate not supplied

The virtual retinal display as a low-vision computer interface: a pilot study.

This pilot study examined the performance of an alternative computer visual interface, the Virtual Retinal Display (VRD), for low-vision use. The VRD scans laser light directly onto the retina, creating a virtual image. Since visually impaired individuals can have difficulty using computer displays, a matched comparison study was done between the VRD and the standard cathode ray tube (CRT) monitor. Reading speed and acuity tests were collected from 13 low-vision volunteers selected to represent the broad range of partially sighted individuals actively involved in the work force. Forty-six percent of subjects had highest visual acuity while viewing the VRD; 30% of subjects had highest acuity viewing the CRT; and 24% of subjects had equal acuity across the two displays. Although mean reading speed across all 13 subjects indicated no significant difference between displays, individual subjects with predominantly optical causes of low vision exhibited clinically important increases in reading speed versus the CRT. However, most subjects with predominantly retinal damage showed a slight disadvantage using the VRD. We give theoretical explanation to the bifurcated results and conclude that for a subset of low-vision users, the VRD technology is very promising as a basis for future low-vision aids.

Adult↗
PubMed2006

Cortically coupled computer vision for rapid image search.

We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.

Algorithms↗
PubMed2005

Using computer vision to help the determination of the gestational age of newborns.

RATIONALE AND OBJECTIVES: This report presents a computational approach to help the gestational age determination of newborns. Gestational age knowledge is fundamental to guide postnatal treatment and increase survival chances of newborns. However, current methods are invasive and do not generate precise results, mainly because they were developed based on nonpremature populations. MATERIALS AND METHODS: We developed an original and noninvasive method to help determination of gestational age based on information supplied by plantar surface images. These images present many details and patterns, but, to date, have not received attention from the image-processing community. We provide a computational tool with suitable facilities to allow the image analysis, either automatically or user driven. This image-processing tool is presented here. RESULTS: The image-processing tool was developed on a user-driven basis. However, as a quantitative experiment, 186 images were processed without user intervention to observe tool behavior in performing different tasks. Although preliminary, experimental results confirm the relationship between plantar surface features and gestational age. CONCLUSION: A prototype of the FootScanAge System is being used and evaluated by experts in neonatology. By means of digital processing of plantar surface images, some characteristics may be shown. Some hypotheses regarding the method have already been confirmed. Also, we show that some well-known image-processing techniques, if appropriately adapted, lead to suitable results when applied to plantar surface images.

Dermatoglyphics↗