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Colloquium
10/30/2009 01:30 pm
CBIM Multipurpose Room

Nonparameteric vessel detection with applications to segmentation, registration and visualization of vascular images

Xenios Papademetris, Depts of Diagnostic Radiology and Biomedical Engineering, Yale University

Faculty Host: Dimitris Metaxas

Abstract

I will describe our nonparametric detection method* for the detection of vascular structures. The major weakness of most existing methods for vessel detection/segmentation is the assumption that at each voxel there exists no more than one cylindrical structure -- an assumption that is violated at vessel branching points. Our proposed method uses a polar parameterization of the local intensity to get around this problem and achieves improved performance over standard methods. I will present results on both synthetic 2D images and 3D MRA animal vascular images. In addition, I will discuss more recent extensions of this work for multiscale vessel detection, non-rigid registration of vascular images, and vascular visualization.

* Qian et al, "A nonparametric vessel detection method for complex vascular structures," Medical Image Analysis, 13(1):49--61, 2009

Bio

Xenophon (Xenios) Papademetris received him Batchelor's degree in Electrical Engineering and Information Sciences from Cambridge University with first class honors. His ph.D. dissertation work was performed under the supervision of professor James Duncan at Yale University, where he was awarded the "Harding Bliss Prize for Excellence in Engineering and Applied Science". Following graduation he continued his research at Yale and was promoted to assistant professor in June 2003. His currently an associate professor of Diagnostic Radiology with a secondary appointment in Biomedical Engineering. His research interests are in the area of medical image analysis and in particular image registration, angiographic image quantification and image-guided interventions. He also coordinates the development of BioImage Suite (www.bioimagesuite.org), which is an advanced software package for medical image analysis/visualization.

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