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摘要:
人体医学热红外图像直观反映了人体表面的温度分布，通过对红外图像进行深入分析能够提供智能化疾病辅助诊断依据。本文根据实际医学红外图像分析的需要，提出了上下半身图像拼接和人体部位划分两种预处理算法。在图像拼接阶段，根据图像采集环境的实际情况，首先对图像进行局部阈值分割，然后采用二值和灰度模板对上下半身图像进行对齐和融合。在区域划分阶段，通过对人体轮廓线进行极值点扫描确定部位区域关键点，并将人体图像划分成头部、躯干及四肢等区域。实验表明，本文所提出的红外图像预处理方法能获得满意的图像拼接及部位划分结果，可有效支持人体温度分布的定量及定性分析。
Abstract:The thermal infrared image of the human body directly reflects the temperature distribution of the human body surface. Based on in-depth analysis, the infrared image can provide intelligent diagnosis assistance for human diseases. This paper proposed two preprocessing algorithms, i.e., upper-lower body image-stitching and body image partitioning, for medical infrared image analysis. In the image stitching stage, the human body is first extracted from the background by local thresholding based on the characteristics of the actual imaging environment. Then the upper and lower body images are aligned and fused using binary and grayscale template matching. In the image partitioning stage, the key points of the part area are determined by the extremum-point analysis of the human contour. The human body is then partitioned into regions including head, trunk, limbs, etc. Experiments show that the proposed preprocessing algorithms produce satisfactory results in image-stitching and portioning, and can effectively support the quantitative and qualitative analysis of human body temperature distribution.
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Overview: Body temperature in medicine is a parameter indicating abnormal activity of human tissues. Over the past few decades, the thermal infrared image of human has attracted extensive interest because it directly reflects the temperature distribution of the human body surface. Based on in-depth analysis, the infrared image can be used to diagnose specific pathologies. In order to carry out thermal infrared image analysis, we need to obtain a complete human body image and perform the correct segmentation of human body parts.
However, efficient algorithms for image stitching and partition of the infrared image are facing challenges and it is worthwhile devoting much effort to this. As can be seen in the picture above, the temperature distribution of the human body in the infrared image is not uniform, the background temperature varies from top to bottom. In addition, the upper and lower body infrared images are not collected at the same time. Thus, the arms may not be in the same position. In the literature, there are three kinds of algorithms for image registration, i.e., feature-based, intensity-based, and transform-based. As infrared thermal human-body images are of limited resolution, low contrast, and barely noticeable texture, the traditional feature-based algorithm cannot work properly. As the position of the arm may be inconsistent, phase correlation algorithm may cause a large error. Among them, intensity-based algorithm is a preferable method, but some existing problems still need to be solved. On the other part, human feature point extraction algorithms mainly include line-by-line scanning and chain-code-based methods, etc., but they are susceptible to noise.
In an effort to overcome these challenges, we proposed two image processing algorithms. First, a template matching algorithm, which uses both binary and grayscale images, is proposed for image stitching. Second, the extreme point detection algorithm is employed to detect the key points. In the image stitching stage, first of all, we solved an accurate binary image for template matching. Based on the idea of Otsu algorithm, our method performs local threshold segmentation on infrared images by using a horizontal sliding window. Next, the starting line of the overlapping region is determined by the template matching with binarization images. The horizontal offset between the upper and lower body images is eliminated by the template matching with grayscale images. The arm area is registered by point matching in the binary image. Finally, the whole-body image is obtained by image fusion. In the image partitioning stage, the key points of the part area are determined by the extremum-point analysis of the human contour. The human body is then partitioned into regions including head, trunk, limbs, etc. Furthermore, the hand area of the human body is separated by a morphological operation. The separation of the foot area is performed similarly.
Experiments show that the proposed preprocessing algorithms produce satisfactory results in image-stitching and portioning. They can effectively support the quantitative and qualitative analysis of human body temperature distribution, which in turn provide intelligent diagnosis assistance for medical treatment.
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图 1 红外图像采集系统和人体红外图像。(a)舱体和工作台；(b)上半身灰度和伪彩色图像；(c)下半身灰度和伪彩色图像
Figure 1. Infrared imaging system and human-body infrared image. (a) Cabin and workbench; (b) Grayscale and pseudo-color images of upper body; (c) Grayscale and pseudo-color images of lower body
图 3 模板匹配结果。(a)上半身图像及搜索区域；(b)下半身图像及模板区域；(c)重叠区域
Figure 3. Template matching results. (a) Upper body image and search area; (b) Lower body image and template area; (c) Overlapping area
图 4 模板匹配过程。(a)上下半身图像；(b)二值模板匹配；(c)灰度模板匹配；(d)图像拼接结果；(e)图像融合结果
Figure 4. Template matching process. (a) Upper and lower body images; (b) Binary template matching; (c) Grayscale template matching; (d) Image stitching results; (e) Image fusion results
图 6 上半身寻找关键点的曲线分析。(a)二值图中轴线至边界扫描示意图；(b)中轴线至边界的距离图；(c)中轴线至边界的距离梯度图
Figure 6. Curve analysis for searching the upper-body key points. (a) Schematic diagram of the boundary-scan from the central axis of the binary map; (b) Distance map from the central axis to the boundary; (c) Distance gradient from the central axis to the boundary
图 7 手部划分过程示意图及最终划分结果
Figure 7. Schematic diagram of the hand partitioning process and the final result
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