TBIdoc: 3D Content-based CT Image Retrieval System for Traumatic Brain Injury

• Main Authors: Li, Shimiao, Gong, Tianxia, Wang, Jie, Liu, Ruizhe, Tan, Chew Lim, Tze-Yun LEONG, Pang, Boon Chuan, Lim, C. C. Tchoyoson, Lee, Cheng Kiang, Tian, Qi, Zhang, Zhuo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Traumatic brain injury; Content-based image retrieval; Computed tomography; Databases and Information Systems; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3066
• Institution: Singapore Management University

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.