Multi-Feature Clustering Approach for Firearm Wound Identification on CT Images

Lian Luo, Yong Chao, Shuai Liu, Wanjun Shuai, Fei Shang*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Damage evaluation and trajectory analysis are critical to the emergency treatment firearm wound which is disturbed by complex wound shape and heterogeneous filling materials. Consequently, accurate identification firearm wound is essential to evaluate the firearm wound. In this study, a firearm wound identification algorithm based on multi-feature clustering was presented. This identification algorithm was divided into three stages: feature extraction, K-means clustering and Gaussian Mixture Model clustering. Six features were extracted from porcine CT volume data, and clustering results from k-means clustering method were used as the input Gaussian Mixture Model. The average accuracy, sensitivity, specificity and Dice similarity coefficient were 0.92, 0.95, 0.63 and 0.53, respectively. Our results showed that the hybrid method with six features was a potential method to identify complex firearm wound.

源语言英语
主期刊名Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
出版商Institute of Electrical and Electronics Engineers Inc.
1594-1599
页数6
ISBN(电子版)9781728116983
DOI
出版状态已出版 - 8月 2019
活动16th IEEE International Conference on Mechatronics and Automation, ICMA 2019 - Tianjin, 中国
期限: 4 8月 20197 8月 2019

出版系列

姓名Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019

会议

会议16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
国家/地区中国
Tianjin
时期4/08/197/08/19

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引用此

Luo, L., Chao, Y., Liu, S., Shuai, W., & Shang, F. (2019). Multi-Feature Clustering Approach for Firearm Wound Identification on CT Images. 在 Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019 (页码 1594-1599). 文章 8816493 (Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMA.2019.8816493