Cheng Tze-Yuan, Herman Cila
Department of Mechanical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218-2682, USA.
Infrared Phys Technol. 2014 Jan;62:70-80. doi: 10.1016/j.infrared.2013.10.009.
In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.
在医学应用中，红外热成像技术用于通过在稳态条件下定量分析皮肤温度或其随时间的变化（这些信息捕捉在图像序列中）来检测和检查皮肤异常的热特征。然而，在图像采集期间，患者的不自主运动是不可避免的，并且这种运动会破坏对皮肤上任何特定位置的温度测量准确性。在本研究中，提出了一种使用基于模板算法的跟踪方法，以跟踪红外图像序列中受试者的不自主运动。运动跟踪将允许在身体相对于图像帧移动时，将温度变化与身体上的每个空间位置相关联。采用仿射变换模型来估计模板图像的运动参数。应用卢卡斯 - 卡纳德算法来搜索仿射变换的优化参数。在算法中加入加权掩码以确保其跟踪鲁棒性。为了评估跟踪方法的可行性，在我们的实验中测试了两组具有随机平面内运动的红外图像序列。首先考虑了一个稳态（无加热或冷却）红外图像序列，其中皮肤温度与环境处于平衡状态。第二种分析的情况是热恢复红外图像序列，它是在皮肤从60秒冷却中恢复时获取的。通过适当选择模板图像以及模板更新，对于这两个红外图像序列都获得了令人满意的跟踪结果。就满足红外热成像临床应用的要求而言，所实现的跟踪精度很有前景。
