Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions
| Autor: | Hayit Greenspan, Allon Shahar |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
business.industry
Feature vector Gaussian ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Probabilistic logic Pattern recognition Context (language use) Mixture model symbols.namesake Feature (computer vision) symbols Segmentation Artificial intelligence business Minimum description length Mathematics |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540226758 ECCV Workshops CVAMIA and MMBIA |
| DOI: | 10.1007/978-3-540-27816-0_23 |
| Popis: | In this paper we describe the application of a novel statistical video-modeling 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 approximately a year. The validation of the framework was performed by a comparison to an expert radiologist’s manual delineation. |
| Databáze: | OpenAIRE |
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