Analysis of Facial Paralysis Disease using Image Processing Technique

Anguraj, K.; Padma, S.

doi:10.5120/8607-2455

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Anguraj, K. et al [ 18 ] utilize canny edge detection technique to evaluate the level of facial palsy clinical symptoms (i.e. normal, mild or severe).…”

Section: Introductionmentioning

confidence: 99%

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

Barbosa

Lee

et al. 2016

BMC Med Imaging

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BackgroundFacial 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.MethodsWe 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.ResultsQuantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency.ConclusionsFacial 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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“…Anguraj, K. et al [ 18 ] utilize canny edge detection technique to evaluate the level of facial palsy clinical symptoms (i.e. normal, mild or severe).…”

Section: Introductionmentioning

confidence: 99%

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

Barbosa

Lee

et al. 2016

BMC Med Imaging

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“…Multiple subjective diagnostic tools are available to assess the severity of a facial palsy during diagnostic workup and follow-up, such as the House-Brackmann (HB) system, Sunnybrook facial grading system, and Sydney score (113,127,128). The majority of studies analyzing the severity of facial palsy objectively in patients are based on comparing static measurements or asymmetry across 2D photographs, while the patient performs different facial expressions or exercises (e.g., 129,130). Other attempts seek to predict clinical ratings directly.…”

Section: Facial Motion Analysismentioning

confidence: 99%

Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases

Matthews

Jong

Maal

et al. 2022

Annu. Rev. Biomed. Data Sci.

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Deviation from a normal facial shape and symmetry can arise from numerous sources, including physical injury and congenital birth defects. Such abnormalities can have important aesthetic and functional consequences. Furthermore, in clinical genetics distinctive facial appearances are often associated with clinical or genetic diagnoses; the recognition of a characteristic facial appearance can substantially narrow the search space of potential diagnoses for the clinician. Unusual patterns of facial movement and expression can indicate disturbances to normal mechanical functioning or emotional affect. Computational analyses of static and moving 2D and 3D images can serve clinicians and researchers by detecting and describing facial structural, mechanical, and affective abnormalities objectively. In this review we survey traditional and emerging methods of facial analysis, including statistical shape modeling, syndrome classification, modeling clinical face phenotype spaces, and analysis of facial motion and affect. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Anguraj et al [6] utilized Canny edge detection to locate a mouth edge and eyebrow, and Sobel edge detection to find the edges of the lateral canthus and the infraorbital region. Nevertheless, these edge detection techniques are very vulnerable to noise.…”

Section: Introductionmentioning

confidence: 99%

Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

Song

Ding

et al. 2018

Future Internet

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Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician’s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists’ ground truth, and our results matched the neurologists’ level with 97.5% accuracy.

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Analysis of Facial Paralysis Disease using Image Processing Technique

Cited by 7 publications

References 11 publications

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

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

Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases

Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

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