Singh Jaspreet, Arora Ajat Shatru
Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, India.
Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, India.
J Therm Biol. 2018 Jan;71:91-98. doi: 10.1016/j.jtherbio.2017.11.001. Epub 2017 Nov 4.
Segmentation of characteristic facial regions has often been an initial step of thermographic analysis in face recognition and clinical diagnosis. Moreover, fast and accurate thermographic analysis on characteristic areas is highly reliant on segmentation approach. Usually, frontal and lateral projections are used to capture the facial thermograms. The significant role of lateral facial thermography to diagnose the various problems associated with orofacial regions has been remarkable in many studies. So far, no study has presented an automatic approach for the segmentation of characteristic areas in lateral facial thermograms. For this purpose, an automatic approach to segment the characteristic areas in lateral facial thermograms is proposed. The dataset of 140 facial thermograms with 1 left and 1 right lateral view per subject is created. Initially, image binarization is performed using optimal temperature thresholding for better visualization of facial geometry. Then, the automatic localization of characteristic points is performed at two levels, based on (a) geometrical features of the face, and (b) local thermal patterns. At last, the characteristic points and auxiliary points are used to segment the characteristic areas in the orofacial region of the face. To evaluate the segmentation performance of the proposed methodology, the automatically localized characteristic points are compared with manually marked using Euclidean distance based comparison criterion. With the localization error δ≤0.05, the proposed automatic approach shows 92.04% of overall localization accuracy and 85% of overall segmentation accuracy. The key conclusion is that the proposed algorithm can potentially automate the process of thermographic analysis on characteristic areas to assist the diagnosis of problems in the orofacial region.
在人脸识别和临床诊断中，特征面部区域的分割通常是热成像分析的第一步。此外，对特征区域进行快速准确的热成像分析高度依赖于分割方法。通常，使用正面和侧面投影来获取面部热图像。在许多研究中，侧面面部热成像在诊断与口面部区域相关的各种问题方面发挥的重要作用十分显著。到目前为止，尚无研究提出用于分割侧面面部热图像中特征区域的自动方法。为此，本文提出了一种自动分割侧面面部热图像中特征区域的方法。创建了一个包含140张面部热图像的数据集，每个受试者有1张左侧视图和1张右侧视图。首先，使用最佳温度阈值进行图像二值化，以便更好地可视化面部几何形状。然后，基于（a）面部的几何特征和（b）局部热模式，在两个级别上进行特征点的自动定位。最后，使用特征点和辅助点对面部口面部区域的特征区域进行分割。为了评估所提出方法的分割性能，使用基于欧几里得距离的比较标准将自动定位的特征点与手动标记的特征点进行比较。在定位误差δ≤0.05的情况下，所提出的自动方法显示出92.04%的总体定位准确率和85%的总体分割准确率。关键结论是，所提出的算法有可能使特征区域的热成像分析过程自动化，以辅助诊断口面部区域的问题。
