White Matter Segmentation from MR Images in Subjects with Brain Tumours
In this study an automatic White Matter (WM) detection method in Magnetic Resonance (MR) images is presented. The detected WM areas are intended to serve as reference areas for the Regional Cerebral Blood Volume (RCBV) perfusion maps analysis aimed at assessing brain tumour neovasculature. Two MR series, possessing the required WM to Gray Matter (GM) contrast, are analysed: T1-Weighted (T1W) and Fluid Attenuated Inversion Recovery (FLAIR). First, the FLAIR series is subjected to anisotropic diffusion filtering. Next, a two-dimensional histogram of the analysed series is calculated and clustered with the use of Kernelised Fuzzy C-Means (KFCM) clustering. Finally, the clustering results are used as WM seed points for the subsequent region growing, providing the WM masks. The methodology has been tested on 10 studies of subjects with brain tumours diagnosed and compared with the Golden Standard (GS) delineations performed by an expert physician. Three similarity measures have been calculated: sensitivity, specificity and the Dice Similarity Coefficient (DSC). Their values amounted to 67.86%, 97.55% and 69.98%, respectively.
Keywordswhite matter segmentation magnetic resonance images brain tumours
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