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17 March 2008 Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features
Yoshikazu Uchiyama, Xin Gao, Takeshi Hara, Hiroshi Fujita, Hiromichi Ando, Hiroyasu Yamakawa, Takahiko Asano, Hiroki Kato, Toru Iwama, Masayuki Kanematsu, Hiroaki Hoshi
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Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69151Q (2008) https://doi.org/10.1117/12.770008
Event: Medical Imaging, 2008, San Diego, California, United States
Abstract
The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.
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Yoshikazu Uchiyama, Xin Gao, Takeshi Hara, Hiroshi Fujita, Hiromichi Ando, Hiroyasu Yamakawa, Takahiko Asano, Hiroki Kato, Toru Iwama, Masayuki Kanematsu, and Hiroaki Hoshi "Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69151Q (17 March 2008); https://doi.org/10.1117/12.770008
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Cited by 2 scholarly publications.
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KEYWORDS
Computer aided diagnosis and therapy
Image segmentation
Magnetic resonance angiography
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