A torsional eye movement calculation algorithm for low contrast images in video-oculography

Abstract

Video-oculography (VOG) is a frequently used clinical technique to detect eye movements. In this research, head mounted small video-cameras and IR-illumination are employed to image the eye. Many algorithms have been developed to extract horizontal and vertical eye movements from the video images. Designing a method to determine torsional eye movements is a more complex task. The use of IR-wavelengths required for illumination in certain clinical tests results in a very low image contrast. In such images, iris textures are almost invisible, making them unsuited for direct application of standard matching algorithms, which are used to calculate torsional eye movements. This research presents the design and implementation of a robust torsional eye movement detection algorithm for VOG. This algorithm uses a new approach to measure the torsional eye movement and is suitable for low contrast videos. The algorithm is implemented in a clinical device and its performance is compared to that of alternative techniques.

Developing a Diagnostic Decision Support System for Benign Paroxysmal Positional Vertigo Using a Deep-Learning Model

“…If a pupil was found, edge-detection and ellipse-fitting algorithms were used to localize the center of the pupil. The directions of the horizontal and vertical movements were determined by the sum of the transient velocity, as previously described [14,16,17]. The center of the pupil in the first frame of each video clip was used as the reference.…”

Eye Blink Detection System for Paralyzed Patients

“…Region of interest (ROI) of the image is pre-processed, and enhanced. To determine the suitable horizontal and vertical translation and to calculate the degrees of rotation, one of the ROI is shifted with respect to the other ROI [4]. Akshat et.al , developed a VOG system in which camera focused on the eye detects and records the blink that matches the required threshold intensity.…”

Optical eye tracking system for real‐time noninvasive tumor localization in external beam radiotherapy

“…The quantification of ocular torsional movement is performed on the image stream coming from one single camera by applying iris texture template matching. 27,28 The annulusshaped iris region [ Fig. 2(a)], recognized in the original image using prior knowledge on the extracted pupil size, is converted into polar coordinates to obtain a rectified iris image, using the pupil center as the origin of transformation [ Fig.…”

“…The resulting ROI is interpolated with cubic spline curves to achieve subpixel accuracy. Since infrared illumination reduces the contrast of the iris texture, a discretization function based on three intensity levels, i.e., black, gray, and white, is applied to the selected ROI, as suggested by Jansen et al 27 A template is extracted from the iris ROI defined on a reference eye image acquired during treatment planning. The torsional eye movements are estimated by matching the template with the acquired iris image.…”

“…The best match is obtained as the global maximum of a scoring function, which assigns a positive value to matching black or matching white pixels and a negative value to mismatching black and white pixels, disregarding the less informative gray areas. 27,28 The assessed torsion is applied to the eye local reference system around the optic axis, thus completing the eye motion detection in six degrees of freedom ( Fig. 3).…”