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MR image segmentation using vector decomposition and probability techniques: A general model and its application to dual-echo images
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DOI：10.1002/mrm.1910350115.png)
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Abstract
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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 less than or equal to 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.
Keywords:
segmentation
volume measurement
fractional volume
image processing
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