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Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier

BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in na...

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Autores principales: Barbosa, Jocelyn, Lee, Kyubum, Lee, Sunwon, Lodhi, Bilal, Cho, Jae-Gu, Seo, Woo-Keun, Kang, Jaewoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788850/
https://www.ncbi.nlm.nih.gov/pubmed/26968938
http://dx.doi.org/10.1186/s12880-016-0117-0
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author Barbosa, Jocelyn
Lee, Kyubum
Lee, Sunwon
Lodhi, Bilal
Cho, Jae-Gu
Seo, Woo-Keun
Kang, Jaewoo
author_facet Barbosa, Jocelyn
Lee, Kyubum
Lee, Sunwon
Lodhi, Bilal
Cho, Jae-Gu
Seo, Woo-Keun
Kang, Jaewoo
author_sort Barbosa, Jocelyn
collection PubMed
description BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region.
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spelling pubmed-47888502016-03-13 Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier Barbosa, Jocelyn Lee, Kyubum Lee, Sunwon Lodhi, Bilal Cho, Jae-Gu Seo, Woo-Keun Kang, Jaewoo BMC Med Imaging Technical Advance BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region. BioMed Central 2016-03-12 /pmc/articles/PMC4788850/ /pubmed/26968938 http://dx.doi.org/10.1186/s12880-016-0117-0 Text en © Barbosa et al. 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Barbosa, Jocelyn
Lee, Kyubum
Lee, Sunwon
Lodhi, Bilal
Cho, Jae-Gu
Seo, Woo-Keun
Kang, Jaewoo
Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
title Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
title_full Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
title_fullStr Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
title_full_unstemmed Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
title_short Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
title_sort efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788850/
https://www.ncbi.nlm.nih.gov/pubmed/26968938
http://dx.doi.org/10.1186/s12880-016-0117-0
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