Sparse classification of discriminant nystagmus features using combined video-oculography tests and pupil tracking for common vestibular disorder recognition.

Vertigo is a common sign related to a problem with the brain or vestibular system. Detection of ocular nystagmus can be a support indicator to distinguish different vestibular disorders. In order to get reliable and accurate real time measurements from nystagmus response, video-oculography (VOG) plays an important role in the daily clinical examination.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis.

Backgroud And Objective: The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition.

Methods: In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region.

A new preprocessing parameter estimation based on geodesic active contour model for automatic vestibular neuritis diagnosis.

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.