Dr. Hong Zhang Profile

Dr. Hong Zhang

Post-Doctoral Fellow at Carnegie Mellon Univ

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Proceedings Article | 4 March 2011 Paper

Robust and fast abdominal aortic aneurysm centerline detection for rupture risk prediction

Hong Zhang, Ender Finol

Proceedings Volume 7963, 79630J (2011) https://doi.org/10.1117/12.878128

KEYWORDS: Image processing, Image segmentation, Computed tomography, Data processing, Particle filters, Gold, Vascular diseases, Magnetic resonance imaging, Algorithm development, Biomedical optics

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This work describes a robust and fast semi-automatic approach for Abdominal Aortic Aneurysm (AAA) centerline detection. AAA is a vascular disease accompanied by progressive enlargement of the abdominal aorta, which leads to rupture if left untreated, an event that accounts for the 13th leading cause of death in the U.S. The lumen centerline can be used to provide the initial starting points for thrombus segmentation. Different from other methods, which are mostly based on region growing and suffer from problems of leakage and heavy computational burden, we propose a novel method based on online classification. An online version of the adaboost classifier based on steerable features is applied to AAA MRI data sets with a rectangular box enclosing the lumen in the first slice. The classifier is updated during the tracking process by using the testing result of the previous image as the new training data. Unlike traditional offline versions, the online classifier can adjust parameters automatically when a leakage occurs. With the help of integral images on the computation of haar-like features, the method can achieve nearly real time processing (about 2 seconds per image on a standard workstation). Ten ruptured and ten unruptured AAA data sets were processed and the tortuosity of the 20 centerlines was calculated. The correlation coefficient of the tortuosity was calculated to illustrate the significance of the prediction with the proposed method. The mean relative accuracy is 95.68% with a standard deviation of 0.89% when compared to a manual segmentation procedure. The correlation coefficient is 0.394.

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