David Meadows

Merging Psychophysical and Psychometric Theory to Estimate Global Visual State Measures from Forced-Choices

Visual acuity, a forced-choice psychophysical measure of visual spatial resolution, is the sine qua non of clinical visual impairment testing in ophthalmology and optometry patients with visual system disorders ranging from refractive error to retinal, optic nerve, or central visual system pathology. Visual acuity measures are standardized against a norm, but it is well known that visual acuity depends on a variety of stimulus parameters, including contrast and exposure duration. This paper asks if it is possible to estimate a single global visual state measure from visual acuity measures as a function of stimulus parameters that can represent the patients overall visual health state with a single variable. Psychophysical theory (at the sensory level) and psychometric theory (at the decision level) are merged to identify the conditions that must be satisfied to derive a global visual state measure from parameterised visual acuity measures. A global visual state measurement model is developed and tested with forced-choice visual acuity measures from 116 subjects with no visual impairments and 560 subjects with uncorrected refractive error. The results are in agreement with the expectations of the model.

An image-based software tool for screening retinal fundus images using vascular morphology and network transport analysis

As the number of digital retinal fundus images taken each year grows at an increasing rate, there exists a similarly increasing need for automatic eye disease detection through image-based analysis. A new method has been developed for classifying standard color fundus photographs into both healthy and diseased categories. This classification was based on the calculated network fluid conductance, a function of the geometry and connectivity of the vascular segments. To evaluate the network resistance, the retinal vasculature was first manually separated from the background to ensure an accurate representation of the geometry and connectivity. The arterial and venous networks were then semi-automatically separated into two separate binary images. The connectivity of the arterial network was then determined through a series of morphological image operations. The network comprised of segments of vasculature and points of bifurcation, with each segment having a characteristic geometric and fluid properties. Based on the connectivity and fluid resistance of each vascular segment, an arterial network flow conductance was calculated, which described the ease with which blood can pass through a vascular system. In this work, 27 eyes (13 healthy and 14 diabetic) from patients roughly 65 years in age were evaluated using this methodology. Healthy arterial networks exhibited an average fluid conductance of 419 ± 89 μm3/mPa-s while the average network fluid conductance of the diabetic set was 165 ± 87 μm3/mPa-s (p < 0.001). The results of this new image-based software demonstrated an ability to automatically, quantitatively and efficiently screen diseased eyes from color fundus imagery.