Background: Challenges in visual identification of laryngeal disorders lead researchers to investigate new opportunities to help clinical examination. This paper presents an efficient and simple method which extracts and assesses blood vessels on vocal fold tissue in order to serve medical diagnosis.
Methods: The proposed vessel segmentation approach has been designed in order to overcome difficulties raised by design specifications of videolaryngostroboscopy and anatomic structure of vocal fold vasculature. The limited number of medical studies on vocal fold vasculature point out that the direction of blood vessels and amount of vasculature are discriminative features for vocal fold disorders. Therefore, we extracted the features of vessels on the basis of these studies. We represent vessels as vascular vectors and suggest a vector field based measurement that quantifies the orientation pattern of blood vessels towards vocal fold pathologies.
Results: In order to demonstrate the relationship between vessel structure and vocal fold disorders, we performed classification of vocal fold disorders by using only vessel features. A binary tree of Support Vector Machine (SVM) has been exploited for classification. Average recall of proposed vessel extraction method was calculated as 0.82 while healthy, sulcus vocalis, laryngitis classification accuracy of 0.75 was achieved.