ABSTRACT

Automatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician’s determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu’sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.

摘要

医学红外热成像（MITI）诊断过程的自动化和标准化至关重要，因为该领域的医学专家数量非常有限。目前的研究通常需要人工干预。其中一种手动操作需要医生确定身体部位和方向。在这项研究中，研究了医学热图像上的自动姿势和身体部位检测。数据库（957 张热图像 - 59 名患者）分为四类，包括前后视图。首先，直方图均衡化 (HE) 方法仅应用于使用 Otsu 阈值方法确定的身体内的像素。其次，使用 DarkNet-19 架构进行特征提取，以及用于特征选择的主成分分析 (PCA) 和 t 分布随机邻域嵌入 (t-SNE) 方法。最后，检查了各种基于机器学习的分类方法的性能。上肢与下肢、后背与上身前部的分类准确率为100%，下半身前后前后分类的成功率为93.38%。这种方法将改进热图像的自动化过程，以将它们分组以将一个图像与其他图像进行比较，并以更用户友好的方式对标记的图像执行查询。下半身前部分类成功率为93.38%。这种方法将改进热图像的自动化过程，以将它们分组以将一个图像与其他图像进行比较，并以更用户友好的方式对标记的图像执行查询。下半身前部分类成功率为93.38%。这种方法将改进热图像的自动化过程，以将它们分组以将一个图像与其他图像进行比较，并以更用户友好的方式对标记的图像执行查询。