Explosion Scene Forensic Image Interpretation.

J Forensic Sci

Institute of Forensic Science, Ministry of Public Security, No. 17, Muxidi Nanli, Beijing, China, 100038.

Published: July 2019

Image interpretation is an important aspect in the field of forensic science; however, it is seldom reported how to use these techniques in explosion scene forensic investigations. On 12 August 2015, a series of explosions killed 165 people and injured hundreds more at a container storage station at the Port of Tianjin. In this study, we applied image interpretation methods to determine the seat of the explosion by analyzing low-quality video clips of the event. The interpretation fits well with recently published standard operating procedures, including the hypothesis, evaluation, inference, and confirmation. Image processing was adopted to enhance the images while the explosion scene was reconstructed with the same images. Some important features were extracted and utilized to distinguish whether the flashes were caused by reflection or a real blast. We reveal the real explosion location, which guides the overall investigation. The results indicate that image interpretation is a powerful tool for forensic investigators to analyze low-quality images in complicated explosions or fire accidents.

Download full-text PDF

Source
http://dx.doi.org/10.1111/1556-4029.13996DOI Listing

Publication Analysis

Top Keywords

image interpretation
16
explosion scene
12
scene forensic
8
explosion
5
image
5
interpretation
5
forensic
4
forensic image
4
interpretation image
4
interpretation aspect
4

Similar Publications

Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging.

View Article and Find Full Text PDF

Background: Point-of-care ultrasound (POCUS) can be used in a variety of clinical settings and is a safe and powerful tool for ultrasound-trained healthcare providers, such as physicians and nurses; however, the effectiveness of ultrasound education for nursing students remains unclear. This prospective cohort study aimed to examine the sustained educational impact of bladder ultrasound simulation among nursing students.

Methods: To determine whether bladder POCUS simulation exercises sustainably improve the clinical proficiency regarding ultrasound examinations among nursing students, evaluations were conducted before and after the exercise and were compared with those after the 1-month follow-up exercise.

View Article and Find Full Text PDF

Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.

View Article and Find Full Text PDF

Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.

View Article and Find Full Text PDF

Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!