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Analysis of Rotational Vertigo using Video and Image Processing

机译:使用视频和图像处理分析旋转眩晕

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摘要

Vertigo is a common disease, whereas the cause is very complex and wide-ranged. This indicates that high knowledge and skills are indispensable in diagnosis of vertigo. Regular doctor diagnoses vertigo in many cases, however they have little knowledge and skills about vertigo. In addition, the number of vertigo patient is increasing. From these reasons, the demand for supporting diagnosis of vertigo is growing.The purpose of this study is to develop an automated computer-aided diagnostic system of vertigo by video and image processing. One of the most important indicators in diagnosing vertigo is nystagmus, namely involuntary abnormal eye movement. For supporting regular doctor's diagnosis, the system must be easy to use and has high accuracy. Therefore, this paper focuses on analyzing nystagmus by video-oculography(VOG) technique which is a video-based method of measuring eye movements using external infrared CCD camera.Previous study using VOG technique has mainly two problems, the noise from blink and the slow performance. This paper resolves these problems by proposal method for analyzing nystagmus. The proposal method can be divided into four stages: 1. detect blink, 2. estimate pupil position, 3. detect pupil position and radius, and 4. calculate rotation angle of torsional nystagmus. A total of 1000 images for each patient were used for evaluating the validity of proposed algorithm.As a result, noise from blink is completely removed in all patients. The proposed algorithm detects pupil in 100% accuracy in each patient, and detects the occurrence of torsional nystagmus. In conclusion, the results indicate that the proposed algorithm meets the requirements for supporting regular doctor.
机译:眩晕是一种常见疾病,其病因非常复杂且范围广泛。这表明高知识和技能对于眩晕的诊断是必不可少的。常规医生会在许多情况下诊断眩晕,但是他们对眩晕的知识和技能知之甚少。另外,眩晕患者的数量正在增加。由于这些原因,支持眩晕诊断的需求正在增长。 这项研究的目的是通过视频和图像处理开发一个自动的计算机辅助性眩晕诊断系统。眼球震颤是诊断眩晕的最重要指标之一,即眼球非自愿性异常运动。为了支持常规医生的诊断,该系统必须易于使用且具有高精度。因此,本文着重于通过视频眼动(VOG)技术分析眼球震颤,这是一种基于视频的使用外部红外CCD摄像机测量眼球运动的方法。 先前使用VOG技术的研究主要存在两个问题,即眨眼噪声和性能下降。本文通过分析眼球震颤的建议方法解决了这些问题。该提议方法可以分为四个阶段:1.检测眨眼; 2.估计瞳孔位置; 3.检测瞳孔位置和半径;以及4.计算扭转性眼球震颤的旋转角度。每个患者总共使用1000张图像来评估所提出算法的有效性。 结果,所有患者的眨眼噪音都被完全消除。所提出的算法可在每位患者中以100%的准确度检测瞳孔,并检测扭转性眼球震颤的发生。总之,结果表明所提出的算法符合支持常规医生的要求。

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