An automated method for the generation of bloodstain pattern metrics from images of blood spatter patterns.

Automated Bloodstain pattern analysis Metrics Quantitative Segmentation

Journal

Forensic science international
ISSN: 1872-6283
Titre abrégé: Forensic Sci Int
Pays: Ireland
ID NLM: 7902034

Informations de publication

Date de publication:
20 Aug 2024
Historique:
received: 17 01 2024
revised: 08 08 2024
accepted: 17 08 2024
medline: 26 8 2024
pubmed: 26 8 2024
entrez: 24 8 2024
Statut: aheadofprint

Résumé

An improved automated bloodstain pattern analysis method has been developed and validated, which utilises computer vision techniques to identify bloodstains on a plain background within a digital image. The method generates metrics relating to the individual stains as well as the overall pattern, including bloodstain pattern specific metrics such as the gamma angle, circularity, solidity, area of convergence, stain density and pattern linearity. This method provides an objective approach to the analysis of bloodstains and bloodstain patterns and can generate a wealth of quantitative data that is currently not obtainable using manual techniques or other image-based programs currently utilised in the discipline. This method will be useful to analysts and researchers investigating the application of quantitative methods to bloodstain pattern analysis.

Identifiants

pubmed: 39180810
pii: S0379-0738(24)00281-0
doi: 10.1016/j.forsciint.2024.112200
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

112200

Informations de copyright

Copyright © 2024 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Rosalyn Rough (R)

Institute of Environmental Science and Research Ltd (ESR), Christchurch Science Centre, P.O Box 29-181, Christchurch, New Zealand; Department of Computer Science and Software Engineering, Faculty of Engineering, University of Canterbury, Private Bag, Christchurch 4800, New Zealand. Electronic address: rosalyn.rough@esr.cri.nz.

Oliver Batchelor (O)

Department of Computer Science and Software Engineering, Faculty of Engineering, University of Canterbury, Private Bag, Christchurch 4800, New Zealand.

Richard Green (R)

Department of Computer Science and Software Engineering, Faculty of Engineering, University of Canterbury, Private Bag, Christchurch 4800, New Zealand.

Andrew Bainbridge-Smith (A)

Department of Computer Science and Software Engineering, Faculty of Engineering, University of Canterbury, Private Bag, Christchurch 4800, New Zealand.

Classifications MeSH