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Communication dans un congrès
Sift-based sequence registration and flow-based cortical vessel segmentation applied to high resolution optical imaging data
Abstract : 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 (30μm, 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 be directly compared to a rigid registration, whereas the vessel extraction algorithm was tested by verifying flow conservation in vascular branching points. Finally, both methods together proved to improve a lot the estimation of the blood velocity in the vessels.
Littérature citée [9 références]
https://hal-enpc.archives-ouvertes.fr/hal-00835134
Contributeur : Pascal Monasse <>
Soumis le : mardi 18 juin 2013 - 13:54:55
Dernière modification le : jeudi 15 octobre 2020 - 14:42:03
Archivage à long terme le : : jeudi 19 septembre 2013 - 04:09:47
Contributeur : Pascal Monasse <>
Soumis le : mardi 18 juin 2013 - 13:54:55
Dernière modification le : jeudi 15 octobre 2020 - 14:42:03
Archivage à long terme le : : jeudi 19 septembre 2013 - 04:09:47
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ISBI08.pdf
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