Automated Segmentation of Head Computed Tomography Images Using FSL

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
Source: Journal of Computer Assisted Tomography - Category: Radiology Tags: Neuroradiology Source Type: research