Biomedical subjects

Charles V Stewart

Publications and source records attributed to Charles V Stewart.

7 recordsLinked to original sources

PubMed2006

Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy.

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology.

Algorithms↗

PubMed2006

Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures.

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at nonvascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched-filter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a six-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements, both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an efficient and effective vessel centerline extraction algorithm.

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PubMed2004

Predictive scheduling algorithms for real-time feature extraction and spatial referencing: application to retinal image sequences.

Real-time spatial referencing is an important alternative to tracking for designing spatially aware ophthalmic instrumentation for procedures such as laser photocoagulation and perimetry. It requires independent, fast registration of each image frame from a digital video stream (1024 x 1024 pixels) to a spatial map of the retina. Recently, we have introduced a spatial referencing algorithm that works in three primary steps: 1) tracing the retinal vasculature to extract image feature (landmarks); 2) invariant indexing to generate hypothesized landmark correspondences and initial transformations; and 3) alignment and verification steps to robustly estimate a 12-parameter quadratic spatial transformation between the image frame and the map. The goal of this paper is to introduce techniques to minimize the amount of computation for successful spatial referencing. The fundamental driving idea is to make feature extraction subservient to registration and, therefore, only produce the information needed for verified, accurate transformations. To this end, the image is analyzed along one-dimensional, vertical and horizontal grid lines to produce a regular sampling of the vasculature, needed for step 3) and to initiate step 1). Tracing of the vascular is then prioritized hierarchically to quickly extract landmarks and groups (constellations) of landmarks for indexing. Finally, the tracing and spatial referencing computations are integrated so that landmark constellations found by tracing are tested immediately. The resulting implementation is an order-of-magnitude faster with the same success rate. The average total computation time is 31.2 ms per image on a 2.2-GHz Pentium Xeon processor.

Algorithms↗

PubMed2004

Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images.

A model-based algorithm, termed exclusion region and position refinement (ERPR), is presented for improving the accuracy and repeatability of estimating the locations where vascular structures branch and cross over, in the context of human retinal images. The goal is two fold. First, accurate morphometry of branching and crossover points (landmarks) in neuronal/vascular structure is important to several areas of biology and medicine. Second, these points are valuable as landmarks for image registration, so improved accuracy and repeatability in estimating their locations and signatures leads to more reliable image registration for applications such as change detection and mosaicing. The ERPR algorithm is shown to reduce the median location error from 2.04 pixels down to 1.1 pixels, while improving the median spread (a measure of repeatability) from 2.09 pixels down to 1.05 pixels. Errors in estimating vessel orientations were similarly reduced from 7.2 degrees down to 3.8 degrees.

Algorithms↗

PubMed2004

Efficient migration of complex off-line computer vision software to real-time system implementation on generic computer hardware.

This paper addresses the problem of migrating large and complex computer vision code bases that have been developed off-line, into efficient real-time implementations avoiding the need for rewriting the software, and the associated costs. Creative linking strategies based on Linux loadable kernel modules are presented to create a simultaneous realization of real-time and off-line frame rate computer vision systems from a single code base. In this approach, systemic predictability is achieved by inserting time-critical components of a user-level executable directly into the kernel as a virtual device driver. This effectively emulates a single process space model that is nonpreemptable, nonpageable, and that has direct access to a powerful set of system-level services. This overall approach is shown to provide the basis for building a predictable frame-rate vision system using commercial off-the-shelf hardware and a standard uniprocessor Linux operating system. Experiments on a frame-rate vision system designed for computer-assisted laser retinal surgery show that this method reduces the variance of observed per-frame central processing unit cycle counts by two orders of magnitude. The conclusion is that when predictable application algorithms are used, it is possible to efficiently migrate to a predictable frame-rate computer vision system.

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PubMed2003

A view-based approach to registration: theory and application to vascular image registration.

This paper presents an approach to registration centered on the notion of a view--a combination of an image resolution, a transformation model, an image region over which the model currently applies, and a set of image primitives from this region. The registration process is divided into three stages: initialization, automatic view generation, and estimation. For a given initial estimate, the latter two alternate until convergence; several initial estimates may be explored. The estimation process uses a novel generalization of the Iterative Closest Point (ICP) technique that simultaneously considers multiple correspondences for each point. View-based registration is applied successfully to alignment of vascular and neuronal images in 2-d and 3-d using similarity, affine, and quadratic transformations.

Algorithms↗

PubMed2003

The dual-bootstrap iterative closest point algorithm with application to retinal image registration.

Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% image overlap.

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