Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 898430, 12 pages
http://dx.doi.org/10.1155/2012/898430
Research Article

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Pavani Davuluri,1 Jie Wu,2 Yang Tang,3 Charles H. Cockrell,3 Kevin R. Ward,4,5 Kayvan Najarian,2,5 and Rosalyn H. Hargraves1

1Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA
2Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
3Department of Radiology, Virginia Commonwealth University, Richmond, VA 23298, USA
4Department of Emergency Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA
5Virginia Commonwealth University Reanimation and Engineering Science Center (VCURES), Richmond, VA 23298, USA

Received 30 April 2012; Accepted 14 June 2012

Academic Editor: Guilherme de Alencar Barreto

Copyright © 2012 Pavani Davuluri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising.