Share Email Print

Proceedings Paper

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images
Author(s): Faranak Aghaei; Stephen R. Ross; Yunzhi Wang; Dee H. Wu; Benjamin O. Cornwell; Bappaditya Ray; Bin Zheng
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Aneurysmal subarachnoid hemorrhage (aSAH) is a form of hemorrhagic stroke that affects middle-aged individuals and associated with significant morbidity and/or mortality especially those presenting with higher clinical and radiologic grades at the time of admission. Previous studies suggested that blood extravasated after aneurysmal rupture was a potentially clinical prognosis factor. But all such studies used qualitative scales to predict prognosis. The purpose of this study is to develop and test a new interactive computer-aided detection (CAD) tool to detect, segment and quantify brain hemorrhage and ventricular cerebrospinal fluid on non-contrasted brain CT images. First, CAD segments brain skull using a multilayer region growing algorithm with adaptively adjusted thresholds. Second, CAD assigns pixels inside the segmented brain region into one of three classes namely, normal brain tissue, blood and fluid. Third, to avoid “black-box” approach and increase accuracy in quantification of these two image markers using CT images with large noise variation in different cases, a graphic User Interface (GUI) was implemented and allows users to visually examine segmentation results. If a user likes to correct any errors (i.e., deleting clinically irrelevant blood or fluid regions, or fill in the holes inside the relevant blood or fluid regions), he/she can manually define the region and select a corresponding correction function. CAD will automatically perform correction and update the computed data. The new CAD tool is now being used in clinical and research settings to estimate various quantitatively radiological parameters/markers to determine radiological severity of aSAH at presentation and correlate the estimations with various homeostatic/metabolic derangements and predict clinical outcome.

Paper Details

Date Published: 13 March 2017
PDF: 8 pages
Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 1013805 (13 March 2017); doi: 10.1117/12.2254094
Show Author Affiliations
Faranak Aghaei, The Univ. of Oklahoma (United States)
Stephen R. Ross, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Yunzhi Wang, The Univ. of Oklahoma (United States)
Dee H. Wu, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Benjamin O. Cornwell, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Bappaditya Ray, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Bin Zheng, The Univ. of Oklahoma (United States)

Published in SPIE Proceedings Vol. 10138:
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Tessa S. Cook; Jianguo Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?