【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。更多还原

【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.更多还原