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

Yoanda Alim Syahbana,Matsubara Yoshitaka,Aoki Mitsuhiro,Morita Hiroyuki,Suzuki Kanade,Yokota Yasunari

doi:10.3390/healthcare9070885

Yoanda Alim Syahbana, Matsubara Yoshitaka + Show 4 more

Open Access

https://doi.org/10.3390/healthcare9070885

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Abstract

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.

Highlights

Dizziness is a common symptom presented by patients in a health examination [1]
In the precise pupil detection step, the search ranges for the pupil center (x0, y0) and radius r were limited to x0 ∈ {x0(t) − 4, x0(t) − 3, . . . , x0(t) + 3, x0(t) + 4}, y0 ∈ {y0(t) − 4, y0(t) − 3, . . . , y0(t) + 3, y0(t) + 4}, and r ∈ {r(t) − 4, r(t) − 3, . . . , r(t) + 3, r(t) + 4}
In the case of a low frequency of nystagmus, which is difficult to evaluate with the naked eye, it could be confirmed and detected in the wavefsmFtohhiFtreogehmitwugehpur.oupnerAdpuei1ipnn4lf1.io.4lFe.rN.ixNgryaausymptrsaetipgad1lmge5amu.nouTsdfshwtswehalovsaismwevfecoafparolmnlhrmaabfmsrefeorpmosclmeoittemuhntehdpeipeonrponortfepohnnopeytsosesnsdteiyandmsgthmmeaotgehurtmoihszdouwodfsnoaftrwsoaVrclaaVipvdpiueedtofpueoioNrrlemNomd.oow1f.o,v1evre,levmlVrebteiriydctniaecttlhao.mleNmopvooreov.mpe2moe8ns,etenadotsfof

Summary

Introduction

Dizziness is a common symptom presented by patients in a health examination [1]. Dizziness represents an unsteady sensation accompanied by a feeling of movement within the head [2]. Based on [7–10], medical specialists can use nystagmus symptoms as a crucial element in identifying the cause of dizziness. A practical method is required to objectively measure eye movements and present the movement as a nystagmus waveform to the medical specialist. An alternative method for eye movement measurement is video-oculography [20–23]. We adopt the video-oculography method to obtain a nystagmus waveform for dizziness diagnosis. The waveform presents estimated eye movement, based on tracked pupil position from the patient’s eye. Tissues and vitreous humor inside the eyeball absorb the diffused light This process causes the pupil to become dark in the video frame.

Data Set Description

Infrared Spot Filling

Findings

Estimation of the Optimal Flatness Parameter q