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 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

Management of peripheral facial nerve palsy

Peripheral facial nerve palsy (FNP) may (secondary FNP) or may not have a detectable cause (Bell’s palsy). Three quarters of peripheral FNP are primary and one quarter secondary. The most prevalent causes of secondary FNP are systemic viral infections, trauma, surgery, diabetes, local infections, tumor, immunological disorders, or drugs. The diagnosis of FNP relies upon the presence of typical symptoms and signs, blood chemical investigations, cerebro-spinal-fluid-investigations, X-ray of the scull and mastoid, cerebral MRI, or nerve conduction studies. Bell’s palsy may be diagnosed after exclusion of all secondary causes, but causes of secondary FNP and Bell’s palsy may coexist. Treatment of secondary FNP is based on the therapy of the underlying disorder. Treatment of Bell’s palsy is controversial due to the lack of large, randomized, controlled, prospective studies. There are indications that steroids or antiviral agents are beneficial but also studies, which show no beneficial effect. Additional measures include eye protection, physiotherapy, acupuncture, botulinum toxin, or possibly surgery. Prognosis of Bell’s palsy is fair with complete recovery in about 80% of the cases, 15% experience some kind of permanent nerve damage and 5% remain with severe sequelae

The crisis of value and the ethical economy

This article argues that the information economy is split in two. On the one hand, there is the traditional capitalist economy that works with monetary incentives. This economy still handles the main part of material production: the production of cars, shoes, computer chips, and the transportation and maintenance of these goods. But immaterial production- the production of the ideas, innovations, experiences and other intangibles that virtually everybody agrees to be the most important source of value and development- is increasingly performed by another economy that does not primarily move according to monetary incentives. We provide a provisional analysis of the value logic of this non-monetary, ethical economy and point at three future scenarios in which the ethical economy challenges the hegemony of global capitalism

Newborn transport around the world

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