Anping Song, Guoliang Xu, Xuehai Ding, Jiaxin Song, Gang Xu, Wu Zhang
School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
Department of Rehabilitation, Shanghai Tenth People's Hospital, Shanghai, 200072, China.
Australas Phys Eng Sci Med. 2017 Dec;40(4):851-860. doi: 10.1007/s13246-017-0597-4. Epub 2017 Oct 31.
Facial nerve paralysis (FNP) is a loss of facial movement due to facial nerve damage, which will lead to significant physical pain and abnormal function in patients. Traditional FNP grading methods are solely based on clinician's judgment and are time-consuming and subjective. Hence, an accurate, quantitative and objective method of evaluating FNP is proposed for constructing a standard system, which will be an invaluable tool for clinicians who treat the patient with FNP. In this paper, we introduce a novel method for quantitative assessment of FNP which combines an effective facial landmark estimation (FLE) algorithm and facial asymmetrical feature (FAF) by processing facial movement image. The facial landmarks can be detected automatically and accurately using FLE. The FAF is based on the angle of key facial landmark connection and mirror degree of multiple regions on human face. Our method provides significant contribution as it describes the displacement of facial organ and the changes of facial organ exposure during performing facial movements. Experiments show that our method is effective, accurate and convenient in practice, which is beneficial to FNP diagnosis and personalized rehabilitation therapy for each patient.
面神经麻痹（FNP）是由于面神经损伤导致的面部运动丧失，这会给患者带来严重的身体疼痛和功能异常。传统的FNP分级方法完全基于临床医生的判断，既耗时又主观。因此，为构建一个标准体系，提出了一种准确、定量且客观的FNP评估方法，这对于治疗FNP患者的临床医生来说将是一个非常有价值的工具。在本文中，我们介绍了一种通过处理面部运动图像来定量评估FNP的新方法，该方法结合了有效的面部标志点估计（FLE）算法和面部不对称特征（FAF）。使用FLE可以自动且准确地检测面部标志点。FAF基于关键面部标志点连接的角度和人脸多个区域的镜像程度。我们的方法具有重要贡献，因为它描述了面部运动过程中面部器官的位移和面部器官暴露情况的变化。实验表明，我们的方法在实践中有效、准确且便捷，这有利于FNP的诊断以及为每位患者提供个性化的康复治疗。
