Automatic Brain Tumor Segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm

The aim of this study was to establish a multi-stage fuzzy c-means (FCM) framework for the automatic and accurate detection of brain tumors from multimodal 3D magnetic resonance image data. The proposed algorithm uses prior information at two points of the execution: (1) the clusters of voxels produced by FCM are classified as possibly tumorous and non-tumorous based on data extracted from train volumes; (2) the choice of FCM parameters (e.g. number of clusters, fuzzy exponent) is supported by train data as well. FCM is applied in two stages: the first stage eliminates the most part of non-tumorous tissues from further processing, while the second stage is intended to accurately extract the tumor tissue clusters. The algorithm was tested on six selected volumes from the BRATS 2012 database. The achieved accuracy is generally characterized by a Dice score in the range of 0.7 to 0.9. Tests have revealed that increasing the size of the train data set slightly improves the overall accuracy.