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Matteo Campana

dblp:73/6752 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image segmentation › object segmentation
cell segmentation
0.112010
Cells Segmentation From 3-D Confocal Images of Early Zebrafish Embryogenesis · IEEE Trans. Image Process. 2010
Image and video processing › biomedical image analysis
medical image analysis
0.112010
Cells Segmentation From 3-D Confocal Images of Early Zebrafish Embryogenesis · IEEE Trans. Image Process. 2010

Methods — techniques the papers use, named apart from their topics

partial differential equations · 0.1edge-preserving filtering · 0.1
YearPublicationVenuePosition
2010 Cells Segmentation From 3-D Confocal Images of Early Zebrafish Embryogenesis
abstract
We designed a strategy for extracting the shapes of cell membranes and nuclei from time lapse confocal images taken throughout early zebrafish embryogenesis using a partial-differential-equation-based segmentation. This segmentation step is a prerequisite for an accurate quantitative analysis of cell morphodynamics during embryogenesis and it is the basis for an integrated understanding of biological processes. The segmentation of embryonic cells requires live zebrafish embryos fluorescently labeled to highlight sub-cellular structures and designing specific algorithms by adapting classical methods to image features. Our strategy includes the following steps: the signal-to-noise ratio is first improved by an edge-preserving filtering, then the cell shape is reconstructed applying a fully automated algorithm based on a generalized version of the Subjective Surfaces technique. Finally we present a procedure for the algorithm validation either from the accuracy and the robustness perspective.
Cecilia Zanella, Matteo Campana, Barbara Rizzi, Camilo Melani, Gonzalo Sanguinetti, Paul Bourgine, Karol Mikula, Nadine Peyriéras, Alessandro Sarti
IEEE Trans. Image Process.2