Automatic Segmentation of High Speed Video Images of Vocal Folds

Abstract

An automatic method for segmenting glottis in high speed endoscopic video (HSV) images of vocal folds is proposed. The method is based on image histogram modeling. Three fundamental problems in automatic histogram based processing of HSV images, which are automatic localization of vocal folds, deformation of the intensity distribution by nonuniform illumination, and ambiguous segmentation when glottal gap is small, are addressed. The problems are solved by using novel masking, illumination, and reflectance modeling methods. The overall algorithm has three stages: masking, illumination modeling, and segmentation. Firstly, a mask is determined based on total variation norm for the region of interest in HSV images. Secondly, a planar illumination model is estimated from consecutive HSV images and reflectance image is obtained. Reflectance images of the masked HSV are used to form a vertical slice image whose reflectance distribution is modeled by a Gaussian mixture model (GMM). Finally, estimated GMM is used to isolate the glottis from the background. Results show that proposed method provides about 94% improvements with respect to manually segmented data in contrast to conventional method which uses Rayleigh intensity distribution in extracting the glottal areas.