Classification of laryngeal disorders based on shape and vascular defects of vocal folds. (1st July 2015)

• Record Type: Journal Article
• Authors: Irem Turkmen, H.; Elif Karsligil, M.; Kocak, Ismail
• Is Part Of: Computers in biology and medicine. Volume 62(2015)
• Page Start: 76
• Page End: 85
• Publication Date: 2015-07-01
• Subjects: Laryngeal image analysis -- Classification of vocal fold disorders -- Histogram of Oriented Gradients -- Vascular vectors -- Vessel centerline extraction -- Measurement of vocal fold shape defects; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.02.001
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Vocal fold disorders such as laryngitis, vocal nodules, and vocal polyps may cause hoarseness, breathing and swallowing difficulties due to vocal fold malfunction. Despite the fact that state of the art medical imaging techniques help physicians to obtain more detailed information, difficulty in differentiating minor anomalies of vocal folds encourages physicians to research new strategies and technologies to aid the diagnostic process. Recent studies on vocal fold disorders note the potential role of the vascular structure of vocal folds in differential diagnosis of anomalies. However, standards of clinical usage of the blood vessels have not been well established yet due to the lack of objective and comprehensive evaluation of the vascular structure. In this paper, we present a novel approach that categorizes vocal folds into healthy, nodule, polyp, sulcus vocalis, and laryngitis classes exploiting visible blood vessels on the superior surface of vocal folds and shapes of vocal fold edges by using image processing techniques and machine learning methods. We first detected the vocal folds on videolaryngostroboscopy images by using Histogram of Oriented Gradients (HOG) descriptors. Then we examined the shape of vocal fold edges in order to provide features such as size and splay portion of mass lesions. We developed a new vessel centerline extraction procedure that is specialized to the vascular structure of vocal folds. Extracted vessel centerlines were evaluated in order to get vascular features of vocal folds, such as the amount of vessels in the longitudinal and transverse form. During the last step, categorization of vocal folds was performed by a novel binary decision tree architecture, which evaluates features of the vocal fold edge shape and vascular structure. The performance of the proposed system was evaluated by using laryngeal images of 70 patients. Sensitivity of 86%, 94%, 80%, 73%, and 76% were obtained for healthy, polyp, nodule, laryngitis, and sulcus vocalis classes, respectively. These results indicate that visible vessels of vocal folds can act as a prognostic marker for vocal fold pathologies, as well as the vocal fold shape features, and may play a critical role in more effective diagnosis. Highlights: Vocal folds on videolaryngostroboscopy images are detected by HOG for examination. Vocal fold images are classified into five laryngeal disorder types. We exploit shape and vascular features of vocal folds for classification. An average classification success rate of 81% is achived. We demonstrate that visible vessels of vocal folds can act as a prognostic marker.