Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis

David Rey 1 Gérard Subsol Hervé Delingette Nicholas Ayache
1 EPIDAURE - Medical imaging and robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Physicians often perform diagnoses based on the evolution of lesions, tumors or anatomical structures through time. The objective of this report is to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between successive temporal images. In studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this report we apply this approach to the automatic detection and segmentation of multiple sclerosis lesions in time series of MRI images of the brain.
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Rapport
RR-3559, INRIA. 1998
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https://hal.inria.fr/inria-00073125
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Soumis le : mercredi 24 mai 2006 - 11:56:02
Dernière modification le : jeudi 11 janvier 2018 - 17:01:41
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David Rey, Gérard Subsol, Hervé Delingette, Nicholas Ayache. Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis. RR-3559, INRIA. 1998. 〈inria-00073125〉

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