Nystagmus Estimation for Dizziness Diagnosis by Pupil Detection and Tracking Using Mexican-Hat-Type Ellipse Pattern Matching

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT.

Human Eye Pupil Detection System for Different IRIS Database Images

The pupil detection system plays a vital role in ophthalmology diagnosis equipments because pupil has a center place of human eye to locate the exact position. To identify the exact human eye pupil region in near infrared (NIR) images, this work proposes the Center of gravity method and its real time FPGA hardware implementation. The proposed work involves global threshold method to segment the pupil region from human eye and the bright spot suppression process removes the light reflections on the pupil due to the IR (Infra red) rays then the morphology dilation process removes unnecessary black pixels other than pupil region on the image. Finally, center of gravity (COG) method provides the exact pupil center coordinate and radius of the human eye. CASIA IRIS V4 and UBIRIS iris database images used in this work and achieved 90-95% of recognition rate.

Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array

Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.