We design a chain of image processing methods to automatically reconstruct the shape of membranes and nuclei from time lapse Multi Photon Laser Scanning Microscopy images, taken throughout early animal embryogenesis. This strategy is a prerequisite for an integrated understanding of morphogenetic processes during organogenesis. In order to produce high contrast images, the embryo is labelled through the expression of fluorescent proteins, the eGFP (enhanced Green Fluorescent Protein) and the mcherry (Red Fluorescent Protein), addressed to membranes and nuclei. The two channels are acquired separately but simultaneously. The noise intrinsically related to the images is removed using the geodesic mean curvature flow, an edge-preserving filtering method which has been proven to be the best suitable for this kind of data. Cells are recognized and located either applying the so-called advection-diffusion equations or the generalized 3D Hough transform on nuclei images. The segmentation of cellular structures is then achieved using variational level set techniques.

CELLS SHAPE RECONSTRUCTION FROM 3-D+TIME LSM IMAGES OF EARLY ZEBRAFISH EMBRYOGENESIS

RIZZI, BARBARA;ZANELLA, CECILIA;CAMPANA, MATTEO;MELANI, CAMILO;SARTI, ALESSANDRO
2008

Abstract

We design a chain of image processing methods to automatically reconstruct the shape of membranes and nuclei from time lapse Multi Photon Laser Scanning Microscopy images, taken throughout early animal embryogenesis. This strategy is a prerequisite for an integrated understanding of morphogenetic processes during organogenesis. In order to produce high contrast images, the embryo is labelled through the expression of fluorescent proteins, the eGFP (enhanced Green Fluorescent Protein) and the mcherry (Red Fluorescent Protein), addressed to membranes and nuclei. The two channels are acquired separately but simultaneously. The noise intrinsically related to the images is removed using the geodesic mean curvature flow, an edge-preserving filtering method which has been proven to be the best suitable for this kind of data. Cells are recognized and located either applying the so-called advection-diffusion equations or the generalized 3D Hough transform on nuclei images. The segmentation of cellular structures is then achieved using variational level set techniques.
Atti del 1° Congresso Nazionale di Bioingegneria
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B. Rizzi; C. Zanella; M. Campana; C. Melani; N. Peyriéras; A. Sarti
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/79796
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