Sift-based sequence registration and flow-based cortical vessel segmentation applied to high resolution optical imaging data

Several functional and biomedical imaging techniques rely on determining hemodynamic variables and their changes in large vascular networks. To do so at micro-vascular resolution requires taking into account the ? usually small but often non-rigid ? mechanical deformations of the imaged vasculature induced by the cardiac pulsation and/or the subjects'body movements. Here, we present two new algorithmic approaches, allowing (i) to efficiently and accurately co-register large sets of such images in a non-rigid manner using Scale-Invariant Feature Transform (SIFT) keypoints, and (ii) to extract blood vessels and their diameters based on blood-flow information using a fast marching algorithm. These methods were applied to optical imaging data of intrinsic signals from awake monkey visual cortex at high spatiotemporal resolution (30?m, 5ms). The movement of red blood cells in the sequences could be enhanced by a Beer-Lambert-based image preprocessing. Our SIFT-based registration could...

• Type: Conference
• Year: 2008
• Where: ISBI
• Authors: Ivo Vanzetta, Mickaël Péchaud, Renaud Keriven, Thomas Deneux
• Keywords: ISBI 2008, Large Vascular Networks, Medical Imaging, Red Blood Cells, Vascular Branching Points