Quantitative Analysis of Healthy and Pathological Vocal Fold Vibrations using an Optical Flow based Waveform

Abstract

The objective assessment of the vocal fold vibrations is important in diagnosing several vocal diseases. Given the high speed of the vibrations, the high speed videoendoscopy is commonly used to capture the vocal fold movements into video recordings. Commonly, two steps are carried out in order to automatically quantify laryngeal parameters and assess the vibra-tions. The first step aims to map the spatial-temporal information contained in the video recordings into a representation that facilitates the analysis of the vibrations. Numerous techniques are reported in the literature but the majority of them require the segmentation of all the images of the video, which is a complex task. The second step aims to quantify laryngeal parameters in order to assess the vibrations. To this aim, most of the existing approaches require an additional processing to the representation in order to deduce those parameters. Furthermore, for some reported representations, the assessment of the symmetry and the periodicity of the vocal fold dynamics needs setting up parameters that are specific to the representation under consideration; which makes difficult the comparison between the existing techniques. To alleviate these problems, the present study investigates the use of a recently proposed representation named optical flow based waveform, in order to objectively quantify the laryngeal parameters. This waveform is retained in this study as it does not require the segmentation of all the images of the video. Furthermore, it will be shown in the present work that the automatic quantification of the vibrations using this waveform can be carried out without applying any additional processing. Moreover, common laryngeal parameters are exploited; hence, no specific parameters are needed to be defined for the automatic assessment of the vibrations. Experiments conducted on healthy and pathological phonation show the accuracy of the waveform. Besides, it is more sensitive to pathological phonation than the state-of-the-art techniques.

Authors and Affiliations

Heyfa Ammar

Keywords

Related Articles

Wavelet Based Image Retrieval Method

A novel method for retrieving image based on color and texture extraction is proposed for improving the accuracy. In this research, we develop a novel image retrieval method based on wavelet transformation to extract the...

Classifying three Communities of Assam Based on Anthropometric Characteristics using R Programming

The study of anthropometric characteristics of different communities plays an important role in design, ergonomics and architecture. As the change of life style, nutrition and ethnic composition of different communities...

Rising Issues in VANET Communication and Security: A State of Art Survey

VANET (Vehicular Adhoc Network) has made an evolution in the transportation hi-tech system in most of the developed countries. VANET plays an important role in an intelligent transportation system (ITS). This paper gives...

Data Flow Sequences: A Revision of Data Flow Diagrams for Modelling Applications using XML

Data Flow Diagrams were developed in the 1970’s as a method of modelling data flow when developing information systems. While DFDs are still being used, the modern web-based which is client-server based means that DFDs a...

Initialization Method for Communication and Data Sharing in P2P Environment Between Wireless Sensor Nodes

Wireless Sensor Networks have increased notewor-thy thought nowadays, rather than wired sensor systems, by presenting multi-useful remote hubs, which are littler in size. However, WSNs correspondence is inclined to negat...

Download PDF file
  • EP ID EP551446
  • DOI 10.14569/IJACSA.2019.0100447
  • Views 77
  • Downloads 0

How To Cite

Heyfa Ammar (2019). Quantitative Analysis of Healthy and Pathological Vocal Fold Vibrations using an Optical Flow based Waveform. International Journal of Advanced Computer Science & Applications, 10(4), 388-393. https://europub.co.uk/articles/-A-551446