ROI Detection with Machine Learning for Glottis Images Captured from High-speed Video-endoscopy

Jun Kubota; Randol Spaulding; Chi Zhu; Krzysztof Izdebski; Yuling Yan

doi:10.20944/preprints201811.0314.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 12 November 2018 / Approved: 13 November 2018 / Online: 13 November 2018 (12:57:10 CET)

Detection of the region of interest (ROI) is a critical step in laryngeal image analysis for the delineation of glottis contour. The process can improve both computational efficiency and accuracy of the image segmentation task, which will facilitate subsequent analysis and characterization of the vocal fold vibration as it correlates with voice quality and pathology. This study aims to develop machine learning based approaches for automatic detection of ROI for glottis image sequences captured by high-speed video-endoscopy (HSV), a clinical laryngeal imaging modality. In particular, we first applied the supporting vector machine (SVM) method using histogram of oriented gradients (HOG) feature descriptor, and second, trained a convolutional neural network (CNN) model for this task. Comparisons are made for both approaches in terms of accuracy of recognition and computation time.

High-speed video-endoscopy, laryngeal image processing, glottis delineation, Machine Learning, CNN

ENGINEERING, Biomedical & Chemical Engineering

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ROI Detection with Machine Learning for Glottis Images Captured from High-speed Video-endoscopy

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