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PubMed1987

Computer automated cell size and shape analysis in cryomicroscopy.

Computer vision techniques have been developed for quantitative analysis of size and shape changes in cells frozen on a cryomicroscope. The analysis is based on implementation of standard serial edge detection algorithms in conjunction with a shape transform to isolate individual cells in complex scenes which may include adjacent and overlying ice crystals. In the present study the sensitivity of the automated analysis procedure is evaluated for images obtained by various microscope optical systems for progressive degrees of subject blurring by defocusing. Size measurements in calibration trials for freezing latex spheres with extracellular ice in the field of view were least sensitive for bright field images, although the most consistent data was obtained by differential interference contrast microscopy. In all cases phase contrast images produced the least accurate data. An example analysis is presented for the freezing of pancreas beta-cells.

Algorithms↗

PubMed2004

Fundamental performance limits in image registration.

The task of image registration is fundamental in image processing. It often is a critical preprocessing step to many modern image processing and computer vision tasks, and many algorithms and techniques have been proposed to address the registration problem. Often, the performances of these techniques have been presented using a variety of relative measures comparing different estimators, leaving open the critical question of overall optimality. In this paper, we present the fundamental performance limits for the problem of image registration as derived from the Cramer-Rao inequality. We compare the experimental performance of several popular methods with respect to this performance bound, and explain the fundamental tradeoff between variance and bias inherent to the problem of image registration. In particular, we derive and explore the bias of the popular gradient-based estimator showing how widely used multiscale methods for improving performance can be explained with this bias expression. Finally, we present experimental simulations showing the general rule-of-thumb performance limits for gradient-based image registration techniques.

Algorithms↗

PubMed2005

Robust estimation of adaptive tensors of curvature by tensor voting.

Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.

Algorithms↗

PubMedDate not supplied

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification.

Bacterial contamination by Listeria monocytogenes not only puts the public at risk, but also is costly for the food-processing industry. Traditional biochemical methods for pathogen identification require complicated sample preparation for reliable results. Optical scattering technology has been used for identification of bacterial cells in suspension, but with only limited success. Therefore, to improve the efficacy of the identification process using our novel imaging approach, we analyze bacterial colonies grown on solid surfaces. The work presented here demonstrates an application of computer-vision and pattern-recognition techniques to classify scatter patterns formed by Listeria colonies. Bacterial colonies are analyzed with a laser scatterometer. Features of circular scatter patterns formed by bacterial colonies illuminated by laser light are characterized using Zernike moment invariants. Principal component analysis and hierarchical clustering are performed on the results of feature extraction. Classification using linear discriminant analysis, partial least squares, and neural networks is capable of separating different strains of Listeria with a low error rate. The demonstrated system is also able to determine automatically the pathogenicity of bacteria on the basis of colony scatter patterns. We conclude that the obtained results are encouraging, and strongly suggest the feasibility of image-based biodetection systems.

Algorithms↗

PubMed1993

An "intelligent" workstation for computer-aided diagnosis.

Computer-aided diagnosis (CAD) involves a computerized analysis of radiographs that is used as a "second opinion" by the radiologist. The approach presented incorporates computer vision and artificial intelligence techniques and includes schemes for the analysis of lung nodules, interstitial infiltrates, and cardiomegaly seen on chest radiographs; masses and clustered microcalcifications on mammograms; and stenoses and blood flow on angiograms. The demonstration of various CAD schemes in chest radiography and mammography on a six-monitor workstation simulates one possible clinical implementation of CAD in radiology. Whether soft- or hard-copy display media are used, the radiologist can refer to the CAD results and still use the original radiograph for the final diagnosis. Although initial impressions of this simulated "intelligent" workstation are encouraging, CAD is still in a preliminary stage of development. Various methods for effectively and efficiently integrating CAD into a clinical radiology department are being investigated.

Angiography↗

PubMed2005

Optimized stereo reconstruction of free-form space curves based on a nonuniform rational B-spline model.

Analytical reconstruction of 3D curves from their stereo images is an important issue in computer vision. We present an optimization framework for such a problem based on a nonuniform rational B-spline (NURBS) curve model that converts reconstruction of a 3D curve into reconstruction of control points and weights of a NURBS representation of the curve, accordingly bypassing the error-prone point-to-point correspondence matching. Perspective invariance of NURBS curves and constraints deduced on stereo NURBS curves are employed to formulate the 3D curve reconstruction problem into a constrained nonlinear optimization. A parallel rectification technique is then adopted to simplify the constraints, and the Levenberg-Marquardt algorithm is applied to search for the optimal solution of the simplified problem. The results from our experiments show that the proposed framework works stably in the presence of different data samplings, randomly posed noise, and partial loss of data and is potentially suitable for real scenes.

Algorithms↗

PubMed2000

Robust multiresolution alignment of MRI brain volumes.

An algorithm for the automatic alignment of MRI volumes of the human brain was developed, based on techniques adopted from the computer vision literature for image motion estimation. Most image registration techniques rely on the assumption that corresponding voxels in the two volumes have equal intensity, which is not true for MRI volumes acquired with different coils and/or pulse sequences. Intensity normalization and contrast equalization were used to minimize the differences between the intensities of the two volumes. However, these preprocessing steps do not correct perfectly for the image differences when using different coils and/or pulse sequences. Hence, the alignment algorithm relies on robust estimation, which automatically ignores voxels where the intensities are sufficiently different in the two volumes. A multiresolution pyramid implementation enables the algorithm to estimate large displacements. The resulting algorithm is used routinely to align MRI volumes acquired using different protocols (3D SPGR and 2D fast spin echo) and different coils (surface and head) to subvoxel accuracy (better than 1 mm).

Algorithms↗

PubMed1997

Application of active contours for photochromic tracer flow extraction.

This paper addresses the implementation of image processing and computer vision techniques to automate tracer flow extraction in images obtained by the photochromic dye technique. This task is important in modeled arterial blood flow studies. Currently, it is performed via manual application of B-spline curve fitting. However, this is a tedious and error-prone procedure and its results are nonreproducible. In the proposed approach, active contours, snakes, are employed in a new curve-fitting method for tracer flow extraction in photochromic images. An algorithm implementing snakes is introduced to automate extraction. Utilizing correlation matching, the algorithm quickly locates and localizes all flow traces in the images. The feasibility of the method for tracer flow extraction is demonstrated. Moreover, results regarding the automation algorithm are presented showing its accuracy and effectiveness. The proposed approach for tracer flow extraction has potential for real-system application.

Algorithms↗

PubMed2005

A 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy.

This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.

Algorithms↗

PubMed2004

A novel approach to 3-D gaze tracking using stereo cameras.

A novel approach to three-dimensional (3-D) gaze tracking using 3-D computer vision techniques is proposed in this paper. This method employs multiple cameras and multiple point light sources to estimate the optical axis of user's eye without using any user-dependent parameters. Thus, it renders the inconvenient system calibration process which may produce possible calibration errors unnecessary. A real-time 3-D gaze tracking system has been developed which can provide 30 gaze measurements per second. Moreover, a simple and accurate calibration method is proposed to calibrate the gaze tracking system. Before using the system, each user only has to stare at a target point for a few (2-3) seconds so that the constant angle between the 3-D line of sight and the optical axis can be estimated. The test results of six subjects showed that the gaze tracking system is very promising achieving an average estimation error of under 1 degrees.

Eye↗

PubMedDate not supplied

Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence.

A set of ultrasonograms of lesions from 200 patients between the ages of 14 and 93 years who underwent mammography followed by ultrasonographic examination and excisional biopsy has been studied with computer vision techniques to improve the ultrasonographic specificity of the diagnosis. Selected features representing the texture of the lesion were calculated and then classified by an artificial neural network. This network was biased toward correctly classifying all the malignant cases at the expense of some misclassification of the benign cases. The network diagnosed the malignant cases with 100% sensitivity and 40% specificity (compared with 0% specificity for the radiologists diagnosing the same set of cases in the breast imaging setting), and tests performed with a leave-one-out technique indicate that the network will generalize well to new cases. This suggests that methods based on neural network classification of texture features show promise for potentially decreasing the number of unnecessary biopsies by a significant amount in patients with sonographically identifiable lesions.

Adolescent↗

PubMed2005

Components of bottom-up gaze allocation in natural images.

Recent research [Parkhurst, D., Law, K., & Niebur, E., 2002. Modeling the role of salience in the allocation of overt visual attention. Vision Research 42 (1) (2002) 107-123] showed that a model of bottom-up visual attention can account in part for the spatial locations fixated by humans while free-viewing complex natural and artificial scenes. That study used a definition of salience based on local detectors with coarse global surround inhibition. Here, we use a similar framework to investigate the roles of several types of non-linear interactions known to exist in visual cortex, and of eccentricity-dependent processing. For each of these, we added a component to the salience model, including richer interactions among orientation-tuned units, both at spatial short range (for clutter reduction) and long range (for contour facilitation), and a detailed model of eccentricity-dependent changes in visual processing. Subjects free-viewed naturalistic and artificial images while their eye movements were recorded, and the resulting fixation locations were compared with the models' predicted salience maps. We found that the proposed interactions indeed play a significant role in the spatiotemporal deployment of attention in natural scenes; about half of the observed inter-subject variance can be explained by these different models. This suggests that attentional guidance does not depend solely on local visual features, but must also include the effects of interactions among features. As models of these interactions become more accurate in predicting behaviorally-relevant salient locations, they become useful to a range of applications in computer vision and human-machine interface design.

Adolescent↗

PubMed1994

Computerized detection of pulmonary nodules in computed tomography images.

RATIONALE AND OBJECTIVES: Interpretation of computed tomographic (CT) scans of the lungs is a time-consuming task that involves visual correlation of possible nodules in one section with those in contiguous sections to distinguish actual nodules from blood vessels. Thus, the authors are developing automated methods to detect nodules on CT images of the thorax. METHODS: The computerized technique uses various computer-vision techniques and a priori information of the morphologic characteristics of pulmonary nodules. In each section, the external thoracic wall and lung boundaries are detected, and the features within the lung boundaries are subjected to gray-level thresholding operations. By analyzing the relationships between features arising at different threshold levels with respect to their shape, size, and location, each feature is assigned a likelihood of being a nodule or a vessel. Features in adjacent sections are compared to resolve ambiguous features. Detected nodule candidates are displayed in three dimensions within the lung. RESULTS: The system provided a sensitivity of 94% for nodule detection and an average of 1.25 false-positive results per case. CONCLUSIONS: Continued development of an automated method for detecting pulmonary nodules in CT scans is expected to aid radiologists in the task of locating nodules in three dimensions.

Artificial Intelligence↗

PubMed2004

Separating reflection components based on chromaticity and noise analysis.

Many algorithms in computer vision assume diffuse only reflections and deem specular reflections to be outliers. However, in the real world, the presence of specular reflections is inevitable since there are many dielectric inhomogeneous objects which have both diffuse and specular reflections. To resolve this problem, we present a method to separate the two reflection components. The method is principally based on the distribution of specular and diffuse points in a two-dimensional maximum chromaticity-intensity space. We found that, by utilizing the space and known illumination color, the problem of reflection component separation can be simplified into the problem of identifying diffuse maximum chromaticity. To be able to identify the diffuse maximum chromaticity correctly, an analysis of the noise is required since most real images suffer from it. Unlike existing methods, the proposed method can separate the reflection components robustly for any kind of surface roughness and light direction.

Algorithms↗

PubMed2004

The use of force histograms for affine-invariant relative position description.

Affine invariant descriptors have been widely used for recognition of objects regardless of their position, size, and orientation in space. Examples of color, texture, and shape descriptors abound in the literature. However, many tasks in computer vision require looking not only at single objects or regions in images but also at their spatial relationships. In an earlier work, we showed that the relative position of two objects can be quantitatively described by a histogram of forces. Here, we study how affine transformations affect this descriptor. The position of an object with respect to another changes when the objects are affine transformed. We analyze the link between 1) the applied affinity, 2) the relative position before transformation (described through a force histogram), and 3) the relative position after transformation. We show that any two of these elements allow the third one to be recovered. Moreover, it is possible to determine whether (or how well) two relative positions are actually related through an affine transformation. If they are not, the affinity that best approximates the unknown transformation can be retrieved, and the quality of the approximation assessed.

Algorithms↗

PubMed2004

A hierarchical self-organizing approach for learning the patterns of motion trajectories.

The understanding and description of object behaviors is a hot topic in computer vision. Trajectory analysis is one of the basic problems in behavior understanding, and the learning of trajectory patterns that can be used to detect anomalies and predict object trajectories is an interesting and important problem in trajectory analysis. In this paper, we present a hierarchical self-organizing neural network model and its application to the learning of trajectory distribution patterns for event recognition. The distribution patterns of trajectories are learnt using a hierarchical self-organizing neural network. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Compared with the existing neural network structures that are used to learn patterns of trajectories, our network structure has smaller scale and faster learning speed, and is thus more effective. Experimental results using two different sets of data demonstrate the accuracy and speed of our hierarchical self-organizing neural network in learning the distribution patterns of object trajectories.

Learning↗

PubMed2003

Integrating content-based visual access methods into a medical case database.

In the computer vision domain, content-based access methods to all forms of multimedia data are a hot research topic. A large number of tools have been developed to find documents in multimedia repositories and to manage the (visual) information that has been created, for example by the Internet. Although no general breakthrough has been achieved with respect to searching diverse databases with automatically extracted features, the techniques have gained acceptance in a few well-defined domains such as image agencies (Corbis) and trademark research. In the medical domain, the number of digital images produced and used for teaching, diagnostics and therapy is rising in a similar way as in other domains. Still, there are only a few image retrieval systems that use automatically extracted visual features for content-based access to medical image databases. This article describes the use of an open source image retrieval system (GIFT) that has been adapted for the use with medical images using the image case database CasImage that has been developed and maintained by the University Hospital of Geneva and that is in routine use. The first results show the potential of this technique to retrieve similar cases from a case database. This is an important task for teaching and might also become important for diagnostics using case-based reasoning, for example. For the use as a diagnostic tool, it is foreseen to specialize the visual features for the domain of lung image retrieval, using high resolution computed tomography (HRCT) images.

Database Management Systems↗

PubMed1995

New and future developments in cerebrovascular ultrasound, magnetic resonance angiography, and related techniques.

Four-dimensional (4D) analysis of atherosclerotic plaque and wall motion, the application of 4D ultrasound to the study of atherogenesis, and the incorporation of ultrasound data into flow models for simulation of cerebrovascular hemodynamics are new frontiers in diagnostic ultrasound that use computer vision and optical flow techniques to exploit the full potential of real-time imaging and Doppler studies. New approaches to improve blood vessel delineation with ultrasound include application of contrast agents, harmonic imaging, and red blood cell density imaging. An assessment of the potential clinical utility of these new developments in cerebrovascular ultrasound requires an analysis of comparable trends in magnetic resonance (MR) technology, eg, rapid advances in the fields of MR angiography, dynamic contrast-enhanced MR, and MR diffusion imaging. Likewise, the value of ultrasound techniques for the measurement of blood flow to evaluate cerebrovascular hemodynamics must be compared to related methods in magnetic resonance, such as dynamic MR inflow tracking. This article addresses several new and future developments in cerebrovascular ultrasound and discusses their relative merits in terms of ongoing research in the field of magnetic resonance angiography, imaging, and related techniques.

Blood Flow Velocity↗