2001 | OriginalPaper | Buchkapitel
Spatio-Temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data
verfasst von : Gabor Székely, Daniel Welti, Guido Gerig, Ernst-Wilhelm Radü, Ludwig Kappos
Erschienen in: Information Processing in Medical Imaging
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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This paper presents a new approach for the automatic segmentation and characterization of active MS lesions in 4D data of multiple sequences. Traditional segmentation of 4D data applies individual 3D spatial segmentation to each image data set, thus not making use of correlation over time. More recently, a time series analysis has been applied to 4D data to reveal active lesions [3]. However, misregistration at tissue borders led to false positive lesion voxels.Lesion development is a complex spatio-temporal process, consequently methods concentrating exclusively on the spatial or temporal aspects of it cannot be expected to provide optimal results. Active MS lesions 44 were extracted from the 4D data in order to quantify MR-based spatiotemporal changes in the brain. A spatio-temporal lesion model generated by principal component analysis allowed robust identification of active MS lesions overcoming the drawbacks of traditional purely spatial or purely temporal segmentation methods.