Department of Forensic Medicine, Criminal Investigation Police University of China, Shenyang, China.
Criminal Investigation Division of Jiading District Bureau of Shanghai Public Security Bureau, Shanghai, China.
J Forensic Sci. 2021 Jan;66(1):356-364. doi: 10.1111/1556-4029.14611. Epub 2020 Oct 28.
The changes of postmortem corneal opacity are often used to roughly estimate the postmortem interval (PMI) in forensic practice. The difficulty associated with this time estimate is the lack of objective means to rapidly quantify postmortem corneal changes in crime scenes. This study constructed a data analysis model of PMI estimation and implemented an intelligent analysis system for examining the sequential changes of postmortem corneal digital images, named Corneal-Smart Phone, which can be used to quickly estimate PMI. The smart phone was used in combination with an attachment device that provided a darkroom environment and a steady light source to capture postmortem corneal images. By segmenting the corneal pupil region images, six color features, Red (R), Green (G), Blue (B), Hue (H), Saturation (S), Brightness (V) and four texture features Contrast (CON), Correlation (COR), Angular Second Moment (ASM), and Homogeneity (HOM), were extracted and correlated with PMI model. The results indicated that CON had the highest correlation with PMI (R = 0.983). No intra/intersubject variation in CON values were observed (p > 0.05). With the increase in ambient temperature or the decrease in humidity, the CON values were increased. PMI prediction error was <3 h within 36 h postmortem and extended to about 6-8 h after 36 h postmortem. The correct classification rate of the blind test samples was 82%. Our study provides a method that combines postmortem corneal image acquisition and digital image analysis to enable users to quickly obtain PMI estimation.
死后角膜混浊的变化常被用来粗略估计法医学中的死后时间间隔(PMI)。这种时间估计的困难在于缺乏客观手段来快速量化犯罪现场死后角膜的变化。本研究构建了 PMI 估计的数据分析模型,并实现了一个智能分析系统,用于检查死后角膜数字图像的连续变化,命名为 Corneal-Smart Phone,可用于快速估计 PMI。该智能手机与附件设备结合使用,附件设备提供暗室环境和稳定的光源以捕获死后角膜图像。通过分割角膜瞳孔区域图像,提取了六个颜色特征(R、G、B、H、S、V)和四个纹理特征(CON、COR、ASM、HOM),并与 PMI 模型相关联。结果表明,CON 与 PMI 具有最高的相关性(R=0.983)。CON 值在个体内/个体间没有变化(p>0.05)。随着环境温度的升高或湿度的降低,CON 值增加。在死后 36 小时内,PMI 预测误差<3 小时,在死后 36 小时后,可延长至约 6-8 小时。盲测试样本的正确分类率为 82%。我们的研究提供了一种结合死后角膜图像采集和数字图像分析的方法,使用户能够快速获得 PMI 估计。