@phdthesis{Goffe11,
author = {Romain {Goffe}},
title = {Pyramides irr\'eguli\`eres descendantes pour la segmentation de grandes images histologiques},
school = {Universit\'e de Poitiers},
year = {2011},
month = {Septembre},
note = {written in french},
keywords = {Topological model; Combinatorial map; Image processing; Medical imaging; Irregular pyramids; Segmentation},
pdf = {Goffe11-THESIS.pdf},
slides = {Goffe11-THESIS-slides.pdf},
abstract = {
Different data acquisition devices produce images of several
gigabytes. Analyzing such large images raises two main
issues. First, the data volume to process forbids a global image
analysis, hence a difficult partitioning problem. Second, a
multi-resolution approach is required to extract global features
at low resolution. For instance, regarding histological images,
scanners' accuracy recently improved such that cellular
structures can be examined on the whole slide. However, produced
images are up to 18\,GB. Besides, considering a tissue as a
particular layout of cells is a global information only
available at low resolution. Thus, these images combine
multi-scale and multi-resolution information.
In this work, we define a topological and hierarchical model
which is suitable for the segmentation of large images. Our work
is based on the previous models of \emph{topological map} and
\emph{combinatorial pyramid}. We introduce the \emph{tiled map}
model in order to represent large partitions and a hierarchical
extension, the \emph{tiled top-down pyramid}, to represent the
duality between multi-scale and multi-resolution
information. Finally, we propose an application of our model for
the segmentation of large images in histology.
}
}
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