Syahbana Yoanda Alim, Yasunari Yokota, Hiroyuki Morita, Mitsuhiro Aoki, Kanade Suzuki, Yoshitaka Matsubara
Graduate School of Engineering, Gifu University, Yanagido 1-1, Gifu 501-1193, Japan.
Computer Engineering, Information Technology Department, Politeknik Caltex Riau, Umban Sari No. 1, Riau 25265, Indonesia.
Healthcare (Basel). 2021 Jul 13;9(7):885. doi: 10.3390/healthcare9070885.
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.
当主诉头晕的患者难以完全睁开眼睛时，使用视频眼震图检测眼球震颤会出现准确性问题。在这种情况下，瞳孔检测和跟踪会影响眼球震颤波形的准确性。在本研究中，为了解决上述问题，我们设计了一种使用模式匹配方法的瞳孔检测方法，该方法使用墨西哥帽型椭圆模式来近似瞳孔。我们从岐阜大学医院获取眼球运动视频，将所提出方法的性能与传统霍夫变换方法的性能进行比较评估。性能结果表明，即使只有20%的瞳孔可见，所提出的方法也能检测和跟踪瞳孔位置。相比之下，传统霍夫变换只有在90%的瞳孔可见时才表现出良好的性能。我们还使用野生标记瞳孔（LPW）数据集对所提出的方法进行评估。结果表明，使用均方误差（MSE）评估时，所提出的方法的准确率为1.47，远低于传统霍夫变换方法的9.53的MSE。我们通过咨询三位医学专家对眼球震颤波形进行专家验证。医学专家一致认为，该波形可以在临床上进行评估，且与他们的诊断不矛盾。
