3,775 research outputs found
Shape and Topology Constrained Image Segmentation with Stochastic Models
The central theme of this thesis has been to develop robust algorithms for the task of image segmentation. All segmentation techniques that have been proposed in this thesis are based on the sound modeling of the image formation process. This approach to image partition enables the derivation of objective functions, which make all modeling assumptions explicit. Based on the Parametric Distributional Clustering (PDC) technique, improved variants have been derived, which explicitly incorporate topological assumptions in the corresponding cost functions. In this thesis, the questions of robustness and generalizability of segmentation solutions have been addressed in an empirical manner, giving comprehensive example sets for both problems. It has been shown, that the PDC framework is indeed capable of producing highly robust image partitions. In the context of PDC-based segmentation, a probabilistic representation of shape has been constructed. Furthermore, likelihood maps for given objects of interest were derived from the PDC cost function. Interpreting the shape information as a prior for the segmentation task, it has been combined with the likelihoods in a Bayesian setting. The resulting posterior probability for the occurrence of an object of a specified semantic category has been demonstrated to achieve excellent segmentation quality on very hard testbeds of images from the Corel gallery
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure
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