Vision function testing for a suprachoroidal retinal prosthesis: effects of image filtering.

OBJECTIVE: One strategy to improve the effectiveness of prosthetic vision devices is to process incoming images to ensure that key information can be perceived by the user. This paper presents the first comprehensive results of vision function testing for a suprachoroidal retinal prosthetic device utilizing of 20 stimulating electrodes. Further, we investigate whether using image filtering can improve results on a light localization task for implanted participants compared to minimal vision processing. No controlled implanted participant studies have yet investigated whether vision processing methods that are not task-specific can lead to improved results. APPROACH: Three participants with profound vision loss from retinitis pigmentosa were implanted with a suprachoroidal retinal prosthesis. All three completed multiple trials of a light localization test, and one participant completed multiple trials of acuity tests. The visual representations used were: Lanczos2 (a high quality Nyquist bandlimited downsampling filter); minimal vision processing (MVP); wide view regional averaging filtering (WV); scrambled; and, system off. MAIN RESULTS: Using Lanczos2, all three participants successfully completed a light localization task and obtained a significantly higher percentage of correct responses than using MVP ([Formula: see text]) or with system off ([Formula: see text]). Further, in a preliminary result using Lanczos2, one participant successfully completed grating acuity and Landolt C tasks, and showed significantly better performance ([Formula: see text]) compared to WV, scrambled and system off on the grating acuity task. SIGNIFICANCE: Participants successfully completed vision tasks using a 20 electrode suprachoroidal retinal prosthesis. Vision processing with a Nyquist bandlimited image filter has shown an advantage for a light localization task. This result suggests that this and targeted, more advanced vision processing schemes may become important components of retinal prostheses to enhance performance. ClinicalTrials.gov Identifier: NCT01503576.

The feasibility of coin motors for use in a vibrotactile display for the blind.

We have tested the potential of three types of vibration motors for use in a tactile vision substitution device for the blind. The motors were of the coin type, which are available off-the-shelf, and are characterized by their affordability, energy efficiency, and ease of implementation. The primary limitation of coin motors is the lack of control they offer over stimulus parameters. Specifically, adjusting the input voltage of a coin motor not only changes the vibration intensity, but also the vibration frequency and duration. This characteristic may result in unpredictable perceptions in psychophysical tests. By using standard psychophysical procedures, we were able to show that the tested coin motors evoked predictable magnitude perceptions across their dynamic range, following Fechner's law as if vibration intensity alone were varied. The best-performing motor was able to generate a median number of 15 available just-noticeable differences, meaning that it was potentially capable of conveying 16 gray levels in its dynamic range. We conclude that coin motors are potential candidates for the construction of a tactile display to substitute for lost vision.

Mobility and low contrast trip hazard avoidance using augmented depth.

OBJECTIVE: We evaluated a novel visual representation for current and near-term prosthetic vision. Augmented depth emphasizes ground obstacles and floor-wall boundaries in a depth-based visual representation. This is achieved by artificially increasing contrast between obstacles and the ground surface via a novel ground plane extraction algorithm specifically designed to preserve low-contrast ground-surface boundaries. APPROACH: The effectiveness of augmented depth was examined in human mobility trials compared against standard intensity-based (Intensity), depth-based (Depth) and random (Random) visual representations. Eight participants with normal vision used simulated prosthetic vision with 20 phosphenes and eight perceivable brightness levels to traverse a course with randomly placed small and low-contrast obstacles on the ground. MAIN RESULTS: The number of collisions was significantly reduced using augmented depth, compared with intensity, depth and random representations (48%, 44% and 72% less collisions, respectively). SIGNIFICANCE: These results indicate that augmented depth may enable safe mobility in the presence of low-contrast obstacles with current and near-term implants. This is the first demonstration that an augmentation of the scene ensuring key objects are visible may provide better outcomes for prosthetic vision.

The role of vision processing in prosthetic vision.

Prosthetic vision provides vision which is reduced in resolution and dynamic range compared to normal human vision. This comes about both due to residual damage to the visual system from the condition that caused vision loss, and due to limitations of current technology. However, even with limitations, prosthetic vision may still be able to support functional performance which is sufficient for tasks which are key to restoring independent living and quality of life. Here vision processing can play a key role, ensuring that information which is critical to the performance of key tasks is available within the capability of the available prosthetic vision. In this paper, we frame vision processing for prosthetic vision, highlight some key areas which present problems in terms of quality of life, and present examples where vision processing can help achieve better outcomes.

Substituting depth for intensity and real-time phosphene rendering: visual navigation under low vision conditions.

Navigation and way finding including obstacle avoidance is difficult when visual perception is limited to low resolution, such as is currently available on a bionic eye. Depth visualisation may be a suitable alternative. Such an approach can be evaluated using simulated phosphenes with a wearable mobile virtual reality kit. In this paper, we present two novel approaches: (i) an implementation of depth visualisation; and, (ii) novel methods for rapid rendering of simulated phosphenes with an empirical comparison between them. Our new software-based method for simulated phosphene rendering shows large speed improvements, facilitating the display in real-time of a large number of phosphenes with size and brightness dependent on pixel intensity, and with customised output dynamic range. Further, we describe the protocol, navigation environment and system used for visual navigation experiments to evaluate the use of depth on low resolution simulations of a bionic eye perceptual experience. Results for these experiments show that a depth-based representation is effective for navigation, and shows significant advantages over intensity-based approaches when overhanging obstacles are present. The results of the experiments were reported in [1], [2].

Ground surface segmentation for navigation with a low resolution visual prosthesis.

We propose the use of ground surface segmentation to enhance the perception of obstacles in low to medium resolution prosthetic visual representations. We apply a recently proposed algorithm for segmenting traversable space in stereo disparity data, and show how such a scheme may be utilised to enhance the distinction between the ground surface and obstructions (in particular, small trip hazards). Qualitative comparisons with intensity and straight depth-based representations highlight advantages for the visualisation of obstacles, offering potential gains for visual navigation with low resolution and low dynamic range visual prostheses.

Hippocampal shape analysis for Alzheimer's disease using an efficient hypothesis test and regularized discriminative deformation.

In this article, we present a framework to perform statistical shape analysis for segmented hippocampi, including an efficient permutation test for detecting subtle class differences, and a regularized discriminative direction method for visualizing the shape discrepancy. Fisher permutation and bootstrap tests are preferred to traditional hypothesis tests which impose assumptions on the distribution of the samples. In this article, an efficient algorithm is adopted to rapidly perform the exact tests. We extend this algorithm to multivariate data by projecting the shape descriptors onto an informative direction that preserves the original discriminative information as much as possible to generate a scalar test statistic. This informative direction is further used to seek a discriminative direction to isolate the discriminative shape difference between classes from the individual variability. Compared with existing methods, the discriminative direction used in this article is regularized by requiring that the shapes deformed along it respect the underlying shape distribution as well as reflecting the essential shape differences between two populations. Hence, a more accurate localization of difference is produced. We apply our methods to analyze the hippocampal shapes for controls and subjects with Alzheimer's disease from the publicly available OASIS MRI database. We show how to localize the shape differences between the two classes.

Identifying anatomical shape difference by regularized discriminative direction.

Identifying the shape difference between two groups of anatomical objects is important for medical image analysis and computer-aided diagnosis. A method called "discriminative direction" in the literature has been proposed to solve this problem. In that method, the shape difference between groups is identified by deforming a shape along the discriminative direction. This paper conducts a thorough study about inferring this discriminative direction in an efficient and accurate way. First, finding the discriminative direction is reformulated as a preimage problem in kernel-based learning. This provides a complementary but conceptually simpler solution than the previous method. More importantly, we find that a shape deforming along the original discriminative direction cannot faithfully maintain its anatomical correctness. This unnecessarily introduces spurious shape differences and leads to inaccurate analysis. To overcome this problem, this paper further proposes a regularized discriminative direction by requiring a shape to conform to its underlying distribution when it deforms. Two different approaches are developed to impose the regularization, one from the perspective of probability distributions and the other from a geometric point of view, and their relationship is discussed. After verifying their superior performance through controlled experiments, we apply the proposed methods to detecting and localizing the hippocampal shape difference between sexes. We get results consistent with other independent research, providing a more compact representation of the shape difference compared with the established discriminative direction method.

Regularized discriminative direction for shape difference analysis.

The "discriminative direction" has been proven useful to reveal the subtle difference between two anatomical shape classes. When a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. However, we observe that such a direction cannot maintain a shape's "anatomical" correctness, introducing spurious difference. To overcome this drawback, we develop a regularized discriminative direction by requiring a shape to conform to its population distribution when it deforms along the discriminative direction. Instead of iterative optimization, an analytic solution is provided to directly work out this direction. Experimental study shows its superior performance in detecting and localizing the difference of hippocampal shapes for sex. The result is supported by other independent research in the same domain.

A study of hippocampal shape difference between genders by efficient hypothesis test and discriminative deformation.

Hypothesis testing is an important way to detect the statistical difference between two populations. In this paper, we use the Fisher permutation and bootstrap tests to differentiate hippocampal shape between genders. These methods are preferred to traditional hypothesis tests which impose assumptions on the distribution of the samples. An efficient algorithm is adopted to rapidly perform the exact tests. We extend this algorithm to multivariate data by projecting the original data onto an "informative direction" to generate a scalar test statistic. This "informative direction" is found to preserve the original discriminative information. This direction is further used in this paper to isolate the discriminative shape difference between classes from the individual variability, achieving a visualization of shape discrepancy.