Authors:
Rose Jean-Loic
1
;
Revol-Muller Chantal
1
;
Odet Christophe
1
and
Christian Reichert
2
Affiliations:
1
CREATIS-LRMN, France
;
2
Institut Camille Jordan UMR 5208, France
Keyword(s):
Image segmentation, Region growing, Region-based criterion, Variational approach, Shape prior.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Region growing is one of the most popular image segmentation methods. The concept of region growing is easily understandable but sometimes criticized for its lack of theorical background. In order to overcome this weakness, we propose to describe region growing in a new framework which is the variational approach. A variational approach is commonly used in image segmentation methods such as active contours or level sets, but is quite original in the context of region growing. We call this method Variational Region Growing. First, we define a region-based criterion. A discrete derivation is applied to this criterion in order to get an evolution rule for the evolving region. The aim of this equation is to guide the evolving region towards a minimum of the criterion. Then, we formalize the iterative process of region growing in the proposed framework. Furthermore, we highlight the relevance of VRG for integrating shape prior. We apply VRG to synthetic and 3D-biomedical images. Results i
llustrate the improvements of VRG compared to classical methods.
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