An exploration framework for segmentation parameter spaces

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 searc...

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Bibliographic Details
Main Authors: Ben Fredj, Sarra, Glatard, T., Casta, C., Clarysse, P.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Clustering algorithms
clustering methods
Deformable models
Genetic algorithms
Image segmentation
Linear matrix inequalities
Three dimensional displays
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Summary: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.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2011.6116087