Fully automated segmentation of cerebrospinal fluid in computed tomography

Abstract

A method is presented for automated delineation and measurement of cerebrospinal fluid (CSF) regions in computed tomographic (CT) sections. Regions of skull and scalp are removed by using a linear discriminant analysis approach. Beam-hardening artifact is reduced by subtracting from each section the average radial intensity profile, characterized by a polynomial function. Remaining intensity gradients are suppressed by implementing CSF segmentation with a local thresholding technique based on maximum-entropy principles. CSF fractions from 12 regions of interest (ROIs) were measured in 10 patients with alcoholic Korsakoff syndrome and 9 normal volunteers. The same ROIs were also assessed by an interactive segmentation method, which enabled the operator to compensate for beam-hardening distortions by selecting suitable threshold values for each ROI. Both methods identified the same ROIs as displaying statistically significant differences between the two subject groups. However, interactive segmentation underestimated sulcal CSF by 20–70%, which was confirmed by applying both methods to CT scans of an anthropomorphic phantom. Hence, in contrast to interactive thresholding, unsupervised segmentation relies on firmly fixed criteria that reduce the influence of beam-hardening distortions and provide more objective results.