DocumentCode :
642493
Title :
Classification of vocal fold nodules and cysts based on vascular defects of vocal folds
Author :
Turkmen, H. Irem ; Karsligil, M.E. ; Kocak, I.
fYear :
2013
fDate :
22-25 Sept. 2013
Firstpage :
1
Lastpage :
6
Abstract :
Physical examination of larynx by using videolaryngostroboscopy provides valuable information for diagnosis of vocal fold pathologies. However difficulty of differentiate nodules and cysts using clinical resources alone motivates physicians to research new strategies. In this paper, we propose a novel approach that performs nodule-cyst classification exploiting visible blood vessels on the superior surface of vocal folds. We first detected the region of vocal folds on videolaryngostroboscopy images and then extracted centerlines of vessel network on vocal folds. We used orientation pattern of vessels for classification. The performance of the proposed system was evaluated using laryngeal images of 21 patients. True positive rates of 76% and 74% were obtained for nodule and cyst classes respectively. These results indicate that visible vessels of vocal folds may play a critical role in more effective diagnosis of vocal fold pathologies like nodule and cyst which may be difficult to differentiate.
Keywords :
biological organs; blood vessels; feature extraction; image classification; medical image processing; laryngeal images; larynx physical examination; true positive rates; vessel network centerline extraction; vessels orientation pattern; videolaryngostroboscopy images; visible blood vessels; vocal fold cysts classification; vocal fold nodules classification; vocal fold pathology diagnosis; vocal folds region detection; vocal folds superior surface; vocal folds vascular defects; Biomedical imaging; Blood vessels; Feature extraction; Image edge detection; Image segmentation; Pathology; Skeleton; Classification of vocal fold pathologies; Histogram of Oriented Gradients; vessel centerline extraction; videolaryngostroboscopy images;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on
Conference_Location :
Southampton
ISSN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2013.6661959
Filename :
6661959
Link To Document :
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