ABSTRACT
INTRODUCTION: Flexible nasendoscopy (FNE) is the principal assessment method for vocal cord movement. Because the procedure is inherently subjective it may not be possible for clinicians to grade the degree of vocal cord movement reliably. The aim of this study was to assess the accuracy and consistency of grading vocal cord movement as viewed via FNE. METHODS: Thirty FNE videos, without sound or clinical information, were assessed by six consultant head and neck surgeons. The surgeons were asked to assess and grade right and left vocal cord movement independently, based on a five-category scale. This process was repeated three times on separate occasions. Agreement and reliability were assessed. RESULTS: Mean overall observed inter-rater agreement was 67.7% (sd 1.9) with the five-category scale, increasing to 91.4% (sd 1.9) when a three-category scale was derived. Mean overall observed intra-rater agreement was 78.3% (sd 9.7) for five categories, increasing to 93.1% (sd 3.3) for three categories. Discriminating vocal cord motion was less reliable using the five-category scale (k = 0.52) than with the three-category scale (k = 0.68). CONCLUSIONS: This study demonstrates quantitatively that it is challenging to accurately and consistently grade subtle differences in vocal cord movement, as proven by the reduced agreement and reliability when using a five-point scale instead of a three-point scale. The study highlights the need for an objective measure to help in the assessment of vocal cord movement.
ABSTRACT
This paper describes a new fully automatic fuzzy multiresolution-based algorithm for cardiac left ventricular (LV) epicardial and endocardial boundary detection and tracking on a sequence of short axis (SA) echocardiographic images of a complete cardiac cycle. This is a necessary step for automatic quantification of cardiac function using echo images. The proposed method is a "center-based" approach in which epicardial and endocardial boundary edge points are searched for on radial lines emanating from the LV center point. The central point of the LV cavity is estimated using a fuzzy-based technique in which the "uncertain" spatial, morphological, and intensity information of the image are represented as fuzzy sets and then combined by fuzzy operators. Edge-detection stage uses multiscale spatial and temporal information in a fuzzy multiresolution framework to identify a single moving edge point for each one of the epicardial and endocardial boundaries over the M radii in the N frames of a complete cardiac cycle. The raw extracted edge points are then processed in the wavelet domain to reduce the effects of noise from the boundaries and papillary muscles from the endocardial boundary extraction process. Finally, a uniform cubic B-spline approximation method is used to define the closed LV boundaries. Experiments with simulated and real echocardiographic images are presented.