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ECCV
2004
Springer

Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions

10 years 16 days ago
Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions
Abstract. In this paper we describe the application of a novel statistical videomodeling scheme to sequences of multiple sclerosis (MS) images taken over time. The analysis of the image-sequence input as a single entity, as opposed to a sequence of separate frames, is a unique feature of the proposed framework. Coherent space-time regions in a four-dimensional feature space (intensity, position (x,y), and time) and corresponding coherent segments in the video content are extracted by unsupervised clustering via Gaussian mixture modeling (GMM). The Expectation-Maximization (EM) algorithm is used to determine the parameters of the model according to the maximum likelihood principle. MS lesions are automatically detected, segmented and tracked in time by context-based classification mechanisms. Qualitative and quantitative results of the proposed methodology are shown for a sequence of 24 T2-weighted MR images, which was acquired from a relapsing-remitting MS patient over a period of appr...
Allon Shahar, Hayit Greenspan
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ECCV
Authors Allon Shahar, Hayit Greenspan
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