Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries

• Published in: Journal of medical systems Vol. 39; no. 9; p. 87
• Main Authors: Reza Soroushmehr, S. M., Davuluri, Pavani, Molaei, Somayeh, Hargraves, Rosalyn Hobson, Tang, Yang, Cockrell, Charles H., Ward, Kevin, Najarian, Kayvan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2015; Springer Nature B.V
• Subjects: Abdomen; Abdominal Injuries - diagnosis; Abdominal Injuries - diagnostic imaging; Accuracy; Algorithms; Assessments; Automation; Health Informatics; Health Sciences; Hemorrhage - diagnosis; Humans; Injuries; Lacerations - diagnosis; Medical imaging; Medical services; Medicine; Medicine & Public Health; Radiographic Image Interpretation, Computer-Assisted - methods; Segmentation; Sensitivity and Specificity; Spleen; Spleen - diagnostic imaging; Spleen - injuries; Statistics for Life Sciences; Systems-Level Quality Improvement; Tomography, X-Ray Computed - methods; Trauma; Trauma Severity Indices; Traumatic abdominal injuries; Spleen segmentation; Seed growing; Edge detection; Contrast enhancement
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-015-0271-x

Spleen segmentation is especially challenging as the majority of solid organs in the abdomen region have similar gray level range. Physician analysis of computed tomography (CT) images to assess abdominal trauma could be very time consuming and hence, automating this process can reduce time to treatment. The proposed method presented in this paper is a fully automated and knowledge based technique that employs anatomical information to accurately segment the spleen in CT images. The spleen detection procedure is proposed to locate the spleen in both healthy and injured cases. In the presence of hemorrhage and laceration, the edge merging technique is used. The accuracy of the method is measured by some criteria such as mis–segmented area, accuracy, specificity and sensitivity. The results show that the proposed spleen segmentation method performs well and outperforms other methods.