Department of Electronics and Communications Engineering, Yıldız Technical University, İstanbul, Turkey.
Department of Electronics and Communications Engineering, Yıldız Technical University, İstanbul, Turkey.
Comput Biol Med. 2018 Jul 1;98:93-99. doi: 10.1016/j.compbiomed.2018.04.023. Epub 2018 May 17.
This paper presents an approach to postmortem interval (PMI) estimation, which is a very debated and complicated area of forensic science. Most of the reported methods to determine PMI in the literature are not practical because of the need for skilled persons and significant amounts of time, and give unsatisfactory results. Additionally, the error margin of PMI estimation increases proportionally with elapsed time after death. It is crucial to develop practical PMI estimation methods for forensic science. In this study, a computational system is developed to determine the PMI of human subjects by investigating postmortem opacity development of the eye. Relevant features from the eye images were extracted using image processing techniques to reflect gradual opacity development. The features were then investigated to predict the time after death using machine learning methods. The experimental results prove that the development of opacity can be utilized as a practical computational tool to determine PMI for human subjects.
本文提出了一种死后间隔时间（PMI）估计方法，这是法医学中一个非常有争议和复杂的领域。文献中报道的大多数确定 PMI 的方法都不实用，因为需要熟练的人员和大量的时间，并且结果并不令人满意。此外，PMI 估计的误差幅度随着死亡后时间的推移而成比例增加。因此，为法医学开发实用的 PMI 估计方法至关重要。在这项研究中，通过研究眼睛的死后不透明度发展，开发了一种计算系统来确定人体的 PMI。使用图像处理技术从眼睛图像中提取相关特征，以反映逐渐不透明度的发展。然后使用机器学习方法研究这些特征，以预测死后时间。实验结果证明，不透明度的发展可以作为一种实用的计算工具，用于确定人体的 PMI。
