Automatic segmentation of subcortical brain structures in MR images using information fusion
| Autor: | J.-Y. Boire, Vincent Barra |
|---|---|
| Přispěvatelé: | Equipe de Recherche en Signal et Imagerie Medicale (ERIM-ERI 14), Université d'Auvergne - Clermont-Ferrand I (UdA)-Institut National de la Santé et de la Recherche Médicale (INSERM) |
| Rok vydání: | 2001 |
| Předmět: |
Computer science
Image processing Fuzzy logic Fuzzy Logic Thalamus [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Image Processing Computer-Assisted Medical imaging Humans Segmentation Computer vision Electrical and Electronic Engineering ComputingMilieux_MISCELLANEOUS Structure (mathematical logic) Brain Mapping Radiological and Ultrasound Technology business.industry Frame (networking) Putamen Process (computing) Image segmentation Magnetic Resonance Imaging Computer Science Applications Systems Integration Artificial intelligence Caudate Nucleus business Algorithms Software |
| Zdroj: | IEEE Transactions on Medical Imaging IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2001, 20 (7), pp.549-558. ⟨10.1109/42.932740⟩ |
| ISSN: | 0278-0062 |
| DOI: | 10.1109/42.932740 |
| Popis: | This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images. |
| Databáze: | OpenAIRE |
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