14 September 1993 Unsupervised classification of multiecho magnetic resonance images of the pediatric brain with implicit spatial and statistical hypotheses validation
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Abstract
We describe an image segmentation method applied to multi-echo MR images which is unsupervised in that the analyst need not specify prototypical tissue signatures to guide the segmentation. It is well known that different tissue types may be distinguished by their signatures in NMR parameter space (spin density and relaxation parameters T1 and T2). Also, normal tissue may be differentiated from abnormal by means of these signatures. Even though pixel intensity is proportional to weighted mixtures of these parameters in real images several researchers feel there is potential for better segmentation results by processing dual-echo images. These images are inherently registered and require no additional time to acquire the image for the second echo. Our segmentation procedure is a multi-step process in which tissue class mean vectors and covariance matrices are first determined by a clustering technique. The goal here is to achieve an intermediate segmentation which may be subject to quantitative validation.
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James B. Perkins, Ian R. Greenshields, Francis DiMario, Gale Ramsby, "Unsupervised classification of multiecho magnetic resonance images of the pediatric brain with implicit spatial and statistical hypotheses validation", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154523; https://doi.org/10.1117/12.154523
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