Ben Slama Amine, Mouelhi Aymen, Sahli Hanene, Manoubi Sondes, Mbarek Chiraz, Trabelsi Hedi, Fnaiech Farhat, Sayadi Mounir
University of Tunis, The National Engineering School of Tunis (ENSIT), Laboratory of Signal Image and Energy Mastery, LR13ES03 (SIME), Tunis, Tunisia.
Department of Oto-Rhino-laryngology, Charles Nicolle Hospital, Tunis, Tunisia.
Artif Intell Med. 2017 Jul;80:48-62. doi: 10.1016/j.artmed.2017.07.005. Epub 2017 Jul 23.
The diagnostic of the vestibular neuritis (VN) presents many difficulties to traditional assessment methods This paper deals with a fully automatic VN diagnostic system based on nystagmus parameter estimation using a pupil detection algorithm. A geodesic active contour model is implemented to find an accurate segmentation region of the pupil. Hence, the novelty of the proposed algorithm is to speed up the standard segmentation by using a specific mask located on the region of interest. This allows a drastically computing time reduction and a great performance and accuracy of the obtained results. After using this fast segmentation algorithm, the obtained estimated parameters are represented in temporal and frequency settings. A useful principal component analysis (PCA) selection procedure is then applied to obtain a reduced number of estimated parameters which are used to train a multi neural network (MNN). Experimental results on 90 eye movement videos show the effectiveness and the accuracy of the proposed estimation algorithm versus previous work.
前庭神经炎（VN）的诊断给传统评估方法带来了诸多困难。本文探讨了一种基于使用瞳孔检测算法的眼球震颤参数估计的全自动VN诊断系统。实现了一个测地线主动轮廓模型来找到瞳孔的精确分割区域。因此，所提算法的新颖之处在于通过使用位于感兴趣区域的特定掩码来加速标准分割。这使得计算时间大幅减少，并且所获结果具有出色的性能和准确性。使用这种快速分割算法后，所获估计参数在时间和频率设置中表示。然后应用一个有用的主成分分析（PCA）选择程序来获得数量减少的估计参数，这些参数用于训练多神经网络（MNN）。对90个眼动视频的实验结果表明，与先前工作相比，所提估计算法具有有效性和准确性。
