@inproceedings{16c38e11ca64419b99fcee5020c4e4d4,
title = "Multi-Feature Clustering Approach for Firearm Wound Identification on CT Images",
abstract = "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.",
keywords = "CT Image, Clustering, Firearm wound, Identification, Multi-feature",
author = "Lian Luo and Yong Chao and Shuai Liu and Wanjun Shuai and Fei Shang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019 ; Conference date: 04-08-2019 Through 07-08-2019",
year = "2019",
month = aug,
doi = "10.1109/ICMA.2019.8816493",
language = "English",
series = "Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1594--1599",
booktitle = "Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019",
address = "United States",
}