A computational approach to estimate postmortem interval using opacity development of eye for human subjects

• Published in: Computers in biology and medicine Vol. 98; pp. 93 - 99
• Main Authors: Cantürk, İsmail, Özyılmaz, Lale
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2018; Elsevier Limited
• Subjects: Algorithms; Artificial intelligence; Classification; Computation; Computer applications; Datasets; Feature extraction; Forensic engineering; Forensic medicine; Forensic science; Forensic sciences; Human subjects; Image processing; Learning algorithms; Machine learning; Methods; Morphology; Opacity; Postmortem interval estimation; Software; Time after death; Time of death; Time of death; Time after death; Forensic medicine; Image processing; Forensic science; Postmortem interval estimation
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.04.023

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. •A computational system is developed to determine PMI of human subjects.•The system incorporates image processing and machine learning methods.•Relevant features from the images were extracted using image processing methods.•The extracted features were used to predict the PMI using machine learning methods.