NDLI: An exploration framework for segmentation parameter spaces

Content Provider	IEEE Xplore Digital Library
Author	Ben Fredj, S. Glatard, T. Casta, C. Clarysse, P.
Copyright Year	2011
Description	Author affiliation: CREATIS - CNRS - UMR 5220 - INSERM U1044 - Université Lyon 1 - INSA Lyon (Ben Fredj, S.; Glatard, T.; Casta, C.; Clarysse, P.)
Abstract	Segmenting 3D images is critical in medical imaging but the parameterization of segmentation algorithms is difficult due to their computation heaviness and complex interactions between the parameters. This paper targets the exploration of deformable-model-based segmentation parameter spaces to search for salient ranges. We propose a framework exploring the parameter space with a genetic algorithm and interactively clustering the segmentation results. The framework only requires a limited number of parameters, it does not make any assumption on the segmentation algorithm and it does not require any ground truth or gold standard. Results obtained on a 3D image of the heart show that the proposed method has good robustness capabilities and that it is able to efficiently exhibit interesting parameter ranges.
Starting Page	2257
Ending Page	2260
File Size	494161
Page Count	4
File Format	PDF
ISBN	9781457713040
ISSN	15224880
e-ISBN	9781457713033
e-ISBN	9781457713026
DOI	10.1109/ICIP.2011.6116087
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-11
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Clustering algorithms Three dimensional displays Biomedical imaging Genetic algorithms Deformable models Linear matrix inequalities genetic algorithms clustering methods
Content Type	Text
Resource Type	Article

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