Segmentation of neuroanatomy in magnetic resonance images

Andrew Simmons; Simon Robert Arridge; G. J. Barker; Paul S. Tofts

doi:10.1117/12.59406

1 June 1992 Segmentation of neuroanatomy in magnetic resonance images

Andrew Simmons, Simon Robert Arridge, G. J. Barker, Paul S. Tofts

Proceedings Volume 1652, Medical Imaging VI: Image Processing; (1992) https://doi.org/10.1117/12.59406
Event: Medical Imaging VI, 1992, Newport Beach, CA, United States

Abstract

Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

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Andrew Simmons, Simon Robert Arridge, G. J. Barker, and Paul S. Tofts "Segmentation of neuroanatomy in magnetic resonance images", Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); https://doi.org/10.1117/12.59406

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