7,964 research outputs found
Determining candidate polyp morphology from CT colonography using a level-set method
In this paper we propose a level-set segmentation for
polyp candidates in Computer Tomography Colongraphy
(CTC). Correct classification of the candidate
polyps into polyp and non-polyp is, in most cases,
evaluated using shape features. Therefore, accurate
recovery of the polyp candidate surface is important
for correct classification. The method presented in
this paper, evolves a curvature and gradient dependent
boundary to recover the surface of the polyp candidate
in a level-set framework. The curvature term
is computed using a combination of the Mean curvature
and the Gaussian curvature. The results of
the algorithm were run through a classifier for two
complete data-sets and returned 100% sensitivity for
polyps greater than 5mm
Semantic 3D Reconstruction with Finite Element Bases
We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Starting from the popular formulation of labeling as a convex relaxation
by functional lifting, we show that FEM discretisation is valid for the most
general case, where the regulariser is anisotropic and non-metric. While our
findings are generic and applicable to different vision problems, we
demonstrate their practical implementation in the context of semantic 3D
reconstruction, where such regularisers have proved particularly beneficial.
The proposed FEM approach leads to a smaller memory footprint as well as faster
computation, and it constitutes a very simple way to enable variable, adaptive
resolution within the same model
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