Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea.
Department of Plastic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.
Sensors (Basel). 2022 Sep 2;22(17):6636. doi: 10.3390/s22176636.
Treatment of facial palsy is essential because neglecting this disorder can lead to serious sequelae and further damage. For an objective evaluation and consistent rehabilitation training program of facial palsy patients, a clinician's evaluation must be simultaneously performed alongside quantitative evaluation. Recent research has evaluated facial palsy using 68 facial landmarks as features. However, facial palsy has numerous features, whereas existing studies use relatively few landmarks; moreover, they do not confirm the degree of improvement in the patient. In addition, as the face of a normal person is not perfectly symmetrical, it must be compared with previous images taken at a different time. Therefore, we introduce three methods to numerically approach measuring the degree of facial palsy after extracting 478 3D facial landmarks from 2D RGB images taken at different times. The proposed numerical approach performs registration to compare the same facial palsy patients at different times. We scale landmarks by performing scale matching before global registration. After scale matching, coarse registration is performed with global registration. Point-to-plane ICP is performed using the transformation matrix obtained from global registration as the initial matrix. After registration, the distance symmetry, angular symmetry, and amount of landmark movement are calculated for the left and right sides of the face. The degree of facial palsy at a certain point in time can be approached numerically and can be compared with the degree of palsy at other times. For the same facial expressions, the degree of facial palsy at different times can be measured through distance and angle symmetry. For different facial expressions, the simultaneous degree of facial palsy in the left and right sides can be compared through the amount of landmark movement. Through experiments, the proposed method was tested using the facial palsy patient database at different times. The experiments involved clinicians and confirmed that using the proposed numerical approach can help assess the progression of facial palsy.
对面瘫的治疗至关重要，因为忽视这种疾病可能会导致严重的后遗症和进一步的损伤。为了对面瘫患者进行客观的评估和一致的康复训练计划，临床医生的评估必须与定量评估同时进行。最近的研究使用 68 个面部标志点对面瘫进行了评估。然而，面瘫有许多特征，而现有的研究使用的标志点相对较少；此外，它们不能确定患者的改善程度。此外，由于正常人的脸不是完全对称的，所以必须与之前在不同时间拍摄的图像进行比较。因此，我们提出了三种方法，通过从不同时间拍摄的二维 RGB 图像中提取 478 个 3D 面部标志点，数值上逼近测量面瘫的程度。所提出的数值方法通过注册来比较不同时间的相同面瘫患者。我们通过在全局注册之前进行比例匹配来缩放标志点。在比例匹配之后，使用全局注册进行粗注册。使用从全局注册获得的变换矩阵作为初始矩阵执行点到平面 ICP。注册后，计算面部左右两侧的距离对称、角度对称和标志点运动的量。可以数值逼近某个时间点的面瘫程度，并与其他时间的面瘫程度进行比较。对于相同的面部表情，可以通过距离和角度对称来测量不同时间的面瘫程度。对于不同的面部表情，可以通过标志点运动的量来比较左右两侧同时面瘫的程度。通过实验，使用不同时间的面瘫患者数据库对所提出的方法进行了测试。实验涉及临床医生，并证实使用所提出的数值方法可以帮助评估面瘫的进展。
