Koprowski Robert
Department of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, ul. Będzińska 39, Sosnowiec 41-200, Poland.
Comput Biol Med. 2015 Jul;62:110-8. doi: 10.1016/j.compbiomed.2015.04.017. Epub 2015 Apr 20.
This paper presents an automatic method for assessing temperature distribution on the patient׳s back using thermal imaging. It discusses the advantages and disadvantages of thermal imaging and presents an algorithm for image analysis and processing that runs automatically and reproducibly.
The proposed new method of image analysis relates to automatic and reproducible analysis of temperature distribution on the patient׳s back. It includes the following steps: (1) detection of the external contours of the human body, (2) identification and recognition of the position of the arms, hips and shoulders, (3) application of a mesh having a fixed number of fields (which automatically adapts to the size of the patient׳s back), (4) analysis of the paraspinal muscles. These steps are preceded by image pre-processing.
Based on a thermal image, this new fully automated algorithm enables to: (1) evaluate temperature distribution in the paraspinal area; (2) pre-assess the degree of lateral spinal curvature in screening, and (3) evaluate anomalies (deviations from the norm) in temperature distribution. The algorithm was practically implemented in Matlab Image Processing Toolbox.
本文介绍了一种使用热成像评估患者背部温度分布的自动方法。讨论了热成像的优缺点，并提出了一种可自动且可重复运行的图像分析与处理算法。
所提出的新图像分析方法涉及对患者背部温度分布进行自动且可重复的分析。它包括以下步骤：（1）检测人体外部轮廓；（2）识别并确定手臂、臀部和肩部的位置；（3）应用具有固定数量区域的网格（该网格会自动适应患者背部的大小）；（4）分析椎旁肌肉。这些步骤之前进行图像预处理。
基于热成像，这种全新的全自动算法能够：（1）评估椎旁区域的温度分布；（2）在筛查中预先评估脊柱侧弯程度；（3）评估温度分布异常（与正常情况的偏差）。该算法已在Matlab图像处理工具箱中实际实现。
