Automated Segmentation of Head Computed Tomography Images Using FSL.

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Abstract

OBJECTIVE

The aim of this study was to investigate the use of one magnetic resonance image-processing tool, FSL, in its ability to perform automated segmentation of computed tomographic images of the brain.

METHODS

Head computed tomography (CT) images were brain extracted and segmented using the FSL tools BET and FAST, respectively. The products of segmentation were analyzed by histogram. The impact of image intensity inhomogeneity correction was investigated using simulated bias fields, 14 routine head CT scans, and selected illustrative clinical cases.

RESULTS

FSL FAST performs direct segmentation of head CT images, permitting quantitation of gray and white matter densities and volumes, achieving a more complete segmentation than masking methods. "Bias field correction" reduced the covariance of image signal intensities of the total brain and gray matter images (P < 0.01). Correction is larger when the effects of beam hardening and radiation scatter are larger, resulting in improved segmentation.

CONCLUSIONS

FSL FAST enables direct segmentation of head CT images.

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