• Conducted cutting edge research in the area of computer vision for medical and industrial applications; expertise in feature based registration, uncertainty modeling and vessel extraction. • Excellent software development and prototyping skills (C/C++, Matlab); integration with software libraries (VXL, ITK, VTK, FLTK); ability to solve problems independently. • Strong publication record in leading international journals (TMI, PAMI) and conferences (CVPR). • Exceptional communication and interpersonal skills with an experience in project leadership; supervised junior graduate students, undergraduate students and interns.

This paper studies the retinal vessel radius estimation and proposes a segmentation method for vessel center lines based on ridge descriptors. The study on radius estimation reveals that the radius estimation by the matched filters based on the second order derivatives of Gaussian kernels is only correct at the vessel center. The relation between the vessel radius and the scale of the Gaussian kernel in the estimation method based on the normalized largest curvature is also studied. The ridge descriptor proposed in this paper contains the normalized largest curvature and the orientations of gradients in the local neighborhood. For vessels of a certain scale, the distribution of the descriptors is assumed to be a normal distribution and is learned from a training set with known truth. Vessel center line segmentation can be then performed based on the distance between the ridge descriptor at candidate pixels and the learned model. Evaluation of the vessel center line segmentation based on the descriptors is done on both DRIVE and STARE databases using the receiver operating characteristic