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Discrete region competition for unknown numbers of connected regions.


Cardinale, J; Paul, G; Sbalzarini, I F (2012). Discrete region competition for unknown numbers of connected regions. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 21(8):3531-3545.

Abstract

We present a discrete, unsupervised multi-regioncompetition algorithm for image segmentation over different energy functionals. The number of regions present in an image does not need to be known a priori, nor their photometric properties. The algorithm jointly estimates the number of regions, their photometries, and their contours. The required regularization is provided by defining a region as a connected set of pixels. The evolving contours in the image are represented by computational particles that move as driven by an energy-minimization algorithm. We present an efficient discrete algorithm that allows minimizing a range of well-known energy functionals under the topological constraint of regions being connected components. The presented framework and algorithms are implemented in the open-source Insight Toolkit (ITK) image-processing library.

Abstract

We present a discrete, unsupervised multi-regioncompetition algorithm for image segmentation over different energy functionals. The number of regions present in an image does not need to be known a priori, nor their photometric properties. The algorithm jointly estimates the number of regions, their photometries, and their contours. The required regularization is provided by defining a region as a connected set of pixels. The evolving contours in the image are represented by computational particles that move as driven by an energy-minimization algorithm. We present an efficient discrete algorithm that allows minimizing a range of well-known energy functionals under the topological constraint of regions being connected components. The presented framework and algorithms are implemented in the open-source Insight Toolkit (ITK) image-processing library.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > LipidX
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2012
Deposited On:05 Jul 2013 10:37
Last Modified:07 Dec 2017 21:38
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1057-7149
Publisher DOI:https://doi.org/10.1109/TIP.2012.2192129
PubMed ID:22481820

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