White matter segmentation from MR images in subjects with brain tumours

Authors:
Paweł Szwarc;Jacek Kawa;Ewa Pietka
Affiliations:
Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland;Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland;Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland
Venue:
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
Year:
2012

Citing 8
Cited 0

Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy C-Means Clustering Algorithm Based on Kernel Method

ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
A new cluster validity measure and its application to image compression

Pattern Analysis & Applications
A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mercer kernel-based clustering in feature space

IEEE Transactions on Neural Networks
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.