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PubMed1998

Flexible docking allowing induced fit in proteins: insights from an open to closed conformational isomers.

Here we dock a ligand onto a receptor surface allowing hinge-bending domain/substructural movements. Our approach mimics and manifests induced fit in molecular recognition. All angular rotations are allowed on the one hand, while a conformational space search is avoided on the other. Rather than dock each of the molecular parts separately with subsequent reconstruction of the consistently docked molecules, all parts are docked simultaneously while still utilizing the position of the hinge from the start. Like pliers closing on a screw, the receptor automatically closes on its ligand in the best surface-matching way. Movements are allowed either in the ligand or in the larger receptor, hence reproducing induced molecular fit. Hinge bending movements are frequently observed when molecules associate. There are numerous examples of open versus closed conformations taking place upon binding. Such movements are observed when the substrate binds to its respective enzyme. In particular, such movements are of interest in allosteric enzymes. The movements can involve entire domains, subdomains, loops, (other) secondary structure elements, or between any groups of atoms connected by flexible joints. We have implemented the hinges at points and at bonds. By allowing 3-dimensional (3-D) rotation at the hinge, several rotations about (consecutive or nearby) bonds are implicitly taken into account. Alternatively, if required, the point rotation can be restricted to bond rotation. Here we illustrate this hinge-bending docking approach and the insight into flexibility it provides on a complex of the calmodulin with its M13 ligand, positioning the hinges either in the ligand or in the larger receptor. This automated and efficient method is adapted from computer vision and robotics. It enables utilizing entire molecular surfaces rather than focusing a priori on active sites. Hence, allows attaining the overall optimally matching surfaces, the extent and type of motions which are involved. Here we do not treat the conformational flexibility of side-chains or of very small pieces of the molecules. Therefore, currently available methods addressing these issues and the method presented here, are complementary to each other, expanding the repertoire of computational docking tools foreseen to aid in studies of recognition, conformational flexibility and drug design.

Algorithms↗

PubMed2001

Fixation could simplify, not complicate, the interpretation of retinal flow.

The visual system must generate a reference frame to relate retinal images in spite of head and eye movements. We show how a reference frame for storing the visual direction and depth of points can be composed from the angles and changes in angles between pairs and triples of points. The representation has no unique origin in 3-D space nor a unique set of cardinal directions (basis vectors). We show how this relative representation could be built up over a series of fixations and for different directions of translation of the observer. Maintaining gaze on a point as the observer translates helps in building up this representation. In our model, retinal flow is divided into changes in eccentricity and changes in meridional angle. The latter, called 'polar angle disparities' for binocular viewing (Weinshall, 1990. Computer Vision Graphics and Image Processing, 49 222-241), can be used to recover the relief structure of the scene in a series of stages up to full Euclidean structure. We show how the direction of heading can be recovered by a similar series of stages.

Computer Simulation↗

PubMed2006

Disruptive coloration, crypsis and edge detection in early visual processing.

Many animals use concealing markings to reduce the risk of predation. These include background pattern matching (crypsis), where the coloration matches a random sample of the background and disruptive patterns, whose effectiveness has been hypothesized to lie in breaking up the body into a series of apparently unrelated objects. We have previously established the effectiveness of disruptive coloration against avian predators, using artificial moth-like stimuli with colours designed to match natural backgrounds as perceived by birds. Here, we investigate the mechanism by which disruptive patterns reduce detectability, using a computational vision model of edge detection applied to photographs of our experimental stimuli, calibrated for bird colour vision. We show that, disruptive coloration is effective by exploiting edge detection algorithms that we use to model early visual processing. Thus, 'false' edges are detected within the body rather than at its periphery, so inhibiting successful detection of the animal's body outline.

Animals↗

PubMed2003

Mathematical modelling of animate and intentional motion.

Our aim is to enable a machine to observe and interpret the behaviour of others. Mathematical models are employed to describe certain biological motions. The main challenge is to design models that are both tractable and meaningful. In the first part we will describe how computer vision techniques, in particular visual tracking, can be applied to recognize a small vocabulary of human actions in a constrained scenario. Mainly the problems of viewpoint and scale invariance need to be overcome to formalize a general framework. Hence the second part of the article is devoted to the question whether a particular human action should be captured in a single complex model or whether it is more promising to make extensive use of semantic knowledge and a collection of low-level models that encode certain motion primitives. Scene context plays a crucial role if we intend to give a higher-level interpretation rather than a low-level physical description of the observed motion. A semantic knowledge base is used to establish the scene context. This approach consists of three main components: visual analysis, the mapping from vision to language and the search of the semantic database. A small number of robust visual detectors is used to generate a higher-level description of the scene. The approach together with a number of results is presented in the third part of this article.

Computers↗

PubMed2006

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

Algorithms↗

PubMed1996

A fast flexible docking method using an incremental construction algorithm.

We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 A rms deviation. It is almost always found among the highest-ranking conformations computed.

Algorithms↗

PubMed1997

Movement, activity and action: the role of knowledge in the perception of motion.

This paper presents several approaches to the machine perception of motion and discusses the role and levels of knowledge in each. In particular, different techniques of motion understanding as focusing on one of movement, activity or action are described. Movements are the most atomic primitives, requiring no contextual or sequence knowledge to be recognized; movement is often addressed using either view-invariant or view-specific geometric techniques. Activity refers to sequences of movements or states, where the only real knowledge required is the statistics of the sequence; much of the recent work in gesture understanding falls within this category of motion perception. Finally, actions are larger-scale events, which typically include interaction with the environment and causal relationships; action understanding straddles the grey division between perception and cognition, computer vision and artificial intelligence. These levels are illustrated with examples drawn mostly from the group's work in understanding motion in video imagery. It is argued that the utility of such a division is that it makes explicit the representational competencies and manipulations necessary for perception.

Motion Perception↗

PubMed2001

CURVES: curve evolution for vessel segmentation.

The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.

Algorithms↗

PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗

PubMed2001

Computerized image analysis: estimation of breast density on mammograms.

An automated image analysis tool is being developed for the estimation of mammographic breast density. This tool may be useful for risk estimation or for monitoring breast density change in prevention or intervention programs. In this preliminary study, a data set of 4-view mammograms from 65 patients was used to evaluate our approach. Breast density analysis was performed on the digitized mammograms in three stages. First, the breast region was segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique was applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification was used to classify the breast images into four classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold was automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area was then estimated. To evaluate the performance of the algorithm, the computer segmentation results were compared to manual segmentation with interactive thresholding by five radiologists. A "true" percent dense area for each mammogram was obtained by averaging the manually segmented areas of the radiologists. We found that the histograms of 6% (8 CC and 8 MLO views) of the breast regions were misclassified by the computer, resulting in poor segmentation of the dense region. For the images with correct classification, the correlation between the computer-estimated percent dense area and the "truth" was 0.94 and 0.91, respectively, for CC and MLO views, with a mean bias of less than 2%. The mean biases of the five radiologists' visual estimates for the same images ranged from 0.1% to 11%. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility of breast density estimation in comparison with the subjective visual assessment by radiologists.

Biophysical Phenomena↗

PubMed2006

A visual probe localization and calibration system for cost-effective computer-aided 3D ultrasound.

The 3D ultrasound systems produce much better reproductions than 2D ultrasound, but their prohibitively high cost deprives many less affluent organization this benefit. This paper proposes using the conventional 2D ultrasound equipment readily available in most hospitals, along with a single conventional digital camera, to construct 3D ultrasound images. The proposed system applies computer vision to extract position information of the ultrasound probe while the scanning takes place. The probe, calibrated in order to calculate the offset of the ultrasound scan from the position of the marker attached to it, is used to scan a number of geometrical objects. Using the proposed system, the 3D volumes of the objects were successfully reconstructed. The system was tested in clinical situations where human body parts were scanned. The results presented, and confirmed by medical staff, are very encouraging for cost-effective implementation of computer-aided 3D ultrasound using a simple setup with 2D ultrasound equipment and a conventional digital camera.

Cost-Benefit Analysis↗

PubMed2005

3D freehand ultrasound reconstruction based on probe trajectory.

3D freehand ultrasound imaging is a very attractive technique in medical examinations and intra-operative stage for its cost and field of view capacities. This technique produces a set of non parallel B-scans which are irregularly distributed in the space. Reconstruction amounts to computing a regular lattice volume and is needed to apply conventional computer vision algorithms like registration. In this paper, a new 3D reconstruction method is presented, taking explicitly into account the probe trajectory. Experiments were conducted on different data sets with various probe motion types and indicate that this technique outperforms classical methods, especially on low acquisition frame rate.

Algorithms↗

PubMed1998

Knowledgebased segmentation.

The segmentation of medical images like CT or MRI scans represents a great challenge to researchers in computer vision, due to the variability of the individual anatomy and the different characteristics of the scanning systems. As an anatomical knowledge base improves the recognition of structures in CT or MRI scans, we chose a knowledge based segmentation in our approach, which will be explained in the following.

Algorithms↗

PubMed2005

Effects of two eye drop products on computer users with subjective ocular discomfort.

BACKGROUND: An increasing number of people seek medical attention for symptoms of visual discomfort due to computer vision syndrome (CVS). We compared the efficacy and adverse event rates of a new eye lubricant, OptiZen (InnoZen, Inc., polysorbate 80 0.5%) and Visine Original (Pfizer Consumer Healthcare, tetrahydrozoline HCl 0.05%). METHODS: In this double-blind parallel arm trial, 50 healthy men and women, ages 18 to 65 years, with symptoms of CVS who use a video display terminal for a minimum of 4 hours per day were randomized to OptiZen (n = 25) or Visine Original (n= 25), 1 to 2 drops b.i.d. for 5 days. The primary end-points were ocular discomfort and adverse events. RESULTS: OptiZen and Visine Original had similar efficacy in alleviating symptoms of ocular discomfort (odds ratio of 1.23 [95% confidence interval, 0.63 to 2.42], P= 0.55). OptiZen and Visine Original were very similar with respect to odds ratios and 95% confidence interval (CI) for each of the measurement times (P= 0.72). Visine Original users reported a significantly higher incidence of temporary ocular stinging/burning immediately after drug instillation (28%, 7/25) than did OptiZen users (4%, 1/24) (P= 0.05). Patients using OptiZen were 89% less likely to have stinging/burning effects than those patients using Visine Original (95% CI: 0.01 to 0.95). DISCUSSION: OptiZen and Visine Original are effective at alleviating ocular discomfort associated with prolonged computer use. Adverse event findings suggest that OptiZen causes less ocular discomfort on instillation, potentially attributable to its milder ingredient profile.

Adolescent↗

PubMed2000

Computer-aided diagnosis for lung cancer.

CAD methods may provide radiologists with tools to obtain more accurate diagnoses for lung cancer. Considerable effort has been devoted to developing CAD tools for CXR; however, these are limited by the fundamental constraints of the projective CXR modality. CT provides far more detailed information that can be exploited better by CAD systems. There has been very little work done in this area to date, although the basic technology has already been developed through the more extensive research in the computer vision areas supported by industry and the military. Initial prototype CT CAD systems have been described that are highly effective in detecting small pulmonary nodules and in predicting malignancy of nodules. CT is now achieving momentum in the study of lung cancer. It has taken time for this modality to gain acceptance because of several factors: higher radiation dose, higher cost, and the novelty of use in this application. It is important to note that the technology for CT scanners is still rapidly evolving. As the speed, resolution, and cost of CT scanners continue to improve, computer techniques for the measurement and analysis of nodules will also achieve corresponding improvements in accuracy and diagnostic utility. Future knowledge-based CT CAD systems will provide detailed analysis of the related conditions of the lungs, such as emphysema, and diagnostic analysis of nodules. The issue is not whether CAD will improve the performance and capabilities of the radiologist, but at what rate their development and the corresponding improvement will occur. Current prototype CAD systems may be considered as tools. As such they will improve the performance of the user/radiologist if they are well engineered and if the user understands their capabilities and limitations. These systems need to be improved by knowledge-based engineering, which is notoriously difficult to implement robustly and requires model refinement and optimization based on a large database of cases. Research should be directed at developing these methods rather than comparing prototype systems with current practices. Future performance should be expected to exceed that of today's grand masters.

Diagnosis, Computer-Assisted↗

PubMed2004

A methodology for extracting objective color from images.

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.

Algorithms↗

PubMed2003

Image registration via level-set motion: applications to atlas-based segmentation.

Image registration is an often encountered problem in various fields including medical imaging, computer vision and image processing. Numerous algorithms for registering image data have been reported in these areas. In this paper, we present a novel curve evolution approach expressed in a level-set framework to achieve image intensity morphing and a simple non-linear PDE for the corresponding coordinate registration. The key features of the intensity morphing model are that (a) it is very fast and (b) existence and uniqueness of the solution for the evolution model are established in a Sobolev space as opposed to using viscosity methods. The salient features of the coordinate registration model are its simplicity and computational efficiency. The intensity morph is easily achieved via evolving level-sets of one image into the level-sets of the other. To explicitly estimate the coordinate transformation between the images, we derive a non-linear PDE-based motion model which can be solved very efficiently. We demonstrate the performance of our algorithm on a variety of images including synthetic and real data. As an application of the PDE-based motion model, atlas based segmentation of hippocampal shape from several MR brain scans is depicted. In each of these experiments, automated hippocampal shape recovery results are validated via manual "expert" segmentations.

Adult↗

PubMed2001

The enhanced LBG algorithm.

Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.

Algorithms↗