Skip Navigation

IEICE Transactions on Information and Systems 2006 E89-D(1):340-350; doi:10.1093/ietisy/e89-d.1.340
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Request Permissions
Google Scholar
Right arrow Articles by KOBASHI, S.
Right arrow Articles by HATA, Y.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Copyright © 2006 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Biological Engineering

Computer-Aided Diagnosis of Intracranial Aneurysms in MRA Images with Case-Based Reasoning

Syoji KOBASHI1, Katsuya KONDO1 and Yutaka HATA1

1 The authors are with University of Hyogo, Himeji-shi, 671–2280 Japan. E-mail: kobashi{at}eng.u-hyogo.ac.jp

Finding intracranial aneurysms plays a key role in preventing serious cerebral diseases such as subarachnoid hemorrhage. For detection of aneurysms, magnetic resonance angiography (MRA) can provide detailed images of arteries non-invasively. However, because over 100 MRA images per subject are required to cover the entire cerebrum, image diagnosis using MRA is very time-consuming and labor-intensive. This article presents a computer-aided diagnosis (CAD) system for finding aneurysms with MRA images. The principal components are identification of aneurysm candidates (= ROIs; regions of interest) from MRA images and estimation of a fuzzy degree for each aneurysm candidate based on a case-based reasoning (CBR). The fuzzy degree indicates whether a candidate is true aneurysm. Our system presents users with a limited number of ROIs that have been sorted in order of fuzzy degree. Thus, this system can decrease the time and the labor required for detecting aneurysms. Experimental results using phantoms indicate that the system can detect all aneurysms at branches of arteries and all saccular aneurysms produced by dilation of a straight artery in 1 direction perpendicular to the principal axis. In a clinical evaluation, performance in finding aneurysms and estimating the fuzzy degree was examined by applying the system to 16 subjects with a total of 19 aneurysms. The experimental results indicate that this CAD system detected all aneurysms except a fusiform aneurysm, and gave high fuzzy degrees and high priorities for the detected aneurysms.

Key Words: intracranial aneurysm, magnetic resonance angiography, case-based reasoning, computer-aided diagnosis, fuzzy logic


Manuscript received February 24, 2005. Manuscript revised July 6, 2005.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
IEICE Trans Inf & SystHome page
R. YOKOYAMA, X. ZHANG, Y. UCHIYAMA, H. FUJITA, T. HARA, X. ZHOU, M. KANEMATSU, T. ASANO, H. KONDO, S. GOSHIMA, et al.
Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images
IEICE Trans D: Information, June 1, 2007; E90-D(6): 943 - 954.
[Abstract] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.