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MR image segmentation using vector decomposition and probability techniques: a general model and its application to dual-echo images.

Y H Kao·J A Sorenson·S S Winkler
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Abstract

A general model is developed for segmenting magnetic resonance images using vector decomposition and probability techniques. Each voxel is assigned fractional volumes of q tissues from p differently weighted images (q < or = p + 1) in the presence of partial-volume mixing, random noise, and other tissues. Compared with the eigenimage method, fewer differently weighted images are needed for segmenting the q tissues, and the contrast-to-noise ratio in the calculated fractional volumes is improved. The model can produce composite tissue-type images similar to that of the probability methods, by comparing the fractional volumes assigned to different tissues on each voxel. A three-tissue (p = 2, q = 3) model is illustrated for segmenting three tissues from dual-echo images. It provides statistical analysis to the algebraic method. A three-compartment phantom is segmented for validation. Two clinical examples are presented.

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ArtifactsBrainBrain EdemaBrain NeoplasmsEcho-Planar ImagingFourier AnalysisHumansMagnetic Resonance ImagingMathematical ComputingModels, AnatomicProbabilitySignal Processing, Computer-AssistedSoftwareComparative StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

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Y H Kao, J A Sorenson, S S Winkler. 1996. MR image segmentation using vector decomposition and probability techniques: a general model and its application to dual-echo images.. https://doi.org/10.1002/mrm.1910350115

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