TY - GEN
T1 - White matter segmentation from MR images in subjects with brain tumours
AU - Szwarc, Paweł
AU - Kawa, Jacek
AU - Pietka, Ewa
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - brain tumours
KW - magnetic resonance images
KW - white matter segmentation
UR - https://www.scopus.com/pages/publications/84862489736
U2 - 10.1007/978-3-642-31196-3_4
DO - 10.1007/978-3-642-31196-3_4
M3 - Conference contribution
AN - SCOPUS:84862489736
SN - 9783642311956
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 46
BT - Information Technologies in Biomedicine - Third International Conference, ITIB 2012, Proceedings
T2 - 3rd International Conference on Information Technologies in Biomedicine, ITIB 2012
Y2 - 11 June 2012 through 13 June 2012
ER -