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Automated segmentation of human brain MR images using a multi-agent approach.

Nathalie Richard 1 Michel Dojat 2, * Catherine Garbay 3
* Corresponding author
1 SIC
TIMC [2011-2015] - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 [2011-2015], Neuroimagerie Fonctionnelle et Metabolique
Abstract : Image interpretation consists in finding a correspondence between radiometric information and symbolic labeling with respect to specific spatial constraints. It is intrinsically a distributed process in terms of goals to be reached, zones in the image to be processed and methods to be applied. To cope the the difficulty inherent in this process, several information processing steps are required to gradually extract information. In this paper we advocate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviors are dynamically adapted depending on their position in the image, of their topographic relationships and of the radiometric information available. The information collected by the agents is gathered, shared via qualitative maps, or used as soon as available by acquaintances. Incremental refinement of interpretation is obtained through a coarse to fine strategy. Our work is essentially focused on radiometry-based tissue interpretation where knowledge is introduced or extracted at several levels to estimate models for tissue-intensity distribution and to cope with noise, intensity non-uniformity and partial volume effect. Several experiments on phantom and real images were performed. A complete volume can be segmented in less than 5 min with about 0.84% accuracy of the segmented reference. Comparison with other techniques demonstrates the potential interest of our approach for magnetic resonance imaging (MRI) brain scan interpretation.
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  • HAL Id : inserm-00402306, version 1
  • PUBMED : 15038368

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Nathalie Richard, Michel Dojat, Catherine Garbay. Automated segmentation of human brain MR images using a multi-agent approach.. Artificial Intelligence in Medicine, Elsevier, 2004, 30 (2), pp.153-75. ⟨inserm-00402306⟩

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