Two step convolutional neural network for automatic glottis localization and segmentation in stroboscopic videos

*, Rahul K and *, Varun Belagali and Ghosh, PrasantaKumar and *, Achuth Kumar V and *, Gopi Kishore Pebbili (2020) Two step convolutional neural network for automatic glottis localization and segmentation in stroboscopic videos. Biomedical Optics Express, 11 (8). pp. 4695-4713. ISSN 21567085

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Abstract

Precise analysis of the vocal fold vibratory pattern in a stroboscopic video plays a key role in the evaluation of voice disorders. Automatic glottis segmentation is one of the preliminary steps in such analysis. In this work, it is divided into two subproblems namely, glottis localization and glottis segmentation. A two step convolutional neural network (CNN) approach is proposed for the automatic glottis segmentation. Data augmentation is carried out using two techniques : 1) Blind rotation (WB), 2) Rotation with respect to glottis orientation (WO). The dataset used in this study contains stroboscopic videos of 18 subjects with Sulcus vocalis, in which the glottis region is annotated by three speech language pathologists (SLPs). The proposed two step CNN approach achieves an average localization accuracy of 90.08% and a mean dice score of 0.65.

Item Type: Article
Uncontrolled Keywords: voice; stroboscopy; glottal segmentation; CNN; DNN
Subjects: Allied Health > Mangalore Campus > Speech and Hearing
Depositing User: KMCMLR User
Date Deposited: 30 Dec 2020 03:55
Last Modified: 30 Dec 2020 03:55
URI: http://eprints.manipal.edu/id/eprint/156151

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