Automatic high-speed video glottis segmentation using salient regions and 3D geodesic active contours

Verbal communication plays an important role in our economy. Laryngeal high-speed videos have emerged as a state of the art method to investigate vocal fold vibrations in the context of voice disorders affecting verbal communication. Segmentation of the glottis from these videos is required to analyze vocal fold vibrations. The vast amount of data produced makes manual segmentation impossible in every day clinical applications. Therefore, computer-aided, automatic segmentation is essential for the use of high-speed videos. In this work a novel, fully automatic glottis segmentation method involving motion compensation, salient region detection and 3D Geodesic Active Contour segmentation is presented. By using color information and establishing spatio-temporal volumes, the method overcomes reported problems in related work regarding low contrast and multiple glottal areas. Efficient computation is achieved by parallelized implementation using graphics adapters and NVidia CUDA. A comparison to the seeded region growing based clinical standard shows the benefits of the proposed method in terms of higher segmentation accuracy on manually annotated evaluation data.