Integrating region and edge information for the automatic segmentation of interventional magnetic resonance images of the shoulder complex
Date
2004
Authors
Tremblay, ME
Branzan Albu, A
Hebert, L
Laurendeau, D
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
This paper proposes a new 2D segmentation
method for MR shoulder images. Due to the
significant length of the image sequences, we aim at
minimizing the user intervention in the segmentation
process. Our method integrates region and edge
information in a coherent manner. In fact, the edge
information is used in the definition of an adaptive
similarity measure for iterative pixel aggregation.
The seeds for the region growing process are defined
automatically, which is essential for processing long
image sequences with variable average brightness.
Moreover, the proposed segmentation approach
implements parallel region growing processes, and
allows for dynamic region merging at successive
iterations. To assess the performance of the
proposed approach, we followed a standard
methodology used for validating 2D segmentation, as
well as a quantitative and qualitative evaluation of
the 3D shoulder model reconstructed from the
segmented image sequences.
Description
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Keywords
Citation
Tremblay, ME, Branzan Albu, A, Hebert L, Laurendeau, D, Integrating region and edge information for the automatic segmentation of interventional magnetic resonance images of the shoulder complex, Proceedings of the First Canadian Conference on Computer and Robot Vision (CRV'04)