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Dual‐echo MRI segmentation using vector decomposition and probability techniques: A two‐tissue model
Author(s) -
Kao YiHsuan,
Sorenson James A.,
Bahn Mark M.,
Winkler Stefan S.
Publication year - 1994
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910320310
Subject(s) - voxel , feature vector , thresholding , pattern recognition (psychology) , imaging phantom , segmentation , artificial intelligence , mathematics , noise (video) , gaussian , probability density function , euclidean distance , computer science , physics , statistics , image (mathematics) , quantum mechanics , optics
We combined a vector decomposition technique with Gaussian probability thresholding in feature space to segment normal brain tissues, tumors, or other abnormalities on dual‐echo MR images. The vector decomposition technique assigns to each voxel a fractional volume for each of two tissues. A probability threshold, based on an assumed Gaussian probability density function describing random noise, isolates a region in feature space for fractional volume calculation that minimizes contamination from other tissues. The calculated fractional volumes are unbiased estimates of the true fractional volumes. The contrast‐to‐noise ratio (CNR) between tissues on the segmented images is the same as the Euclidean norm of CNRs in the original images. The method is capable of segmenting more than two tissues from a set ot dual‐echo images by sequentially analyzing different pairs of tissues. The model is analyzed mathematically and in experiments with a phantom. Two clinical examples are presented.