Segmentation of brain MR images: A self-adaptive online vector quantization approach

We present a fully automatic algorithm for brain magnetic resonance (MR) image segmentation. The three-dimensional (3D) volumetric MR dataset is first interpolated for an adequate local intensity vector on each voxel. Then a morphology dilation filter and region growing technique are applied to extract the region of brain volume, strapping away the skull, scalp and other tissues. The principal component analysis (PCA) is utilized to generate a series of feature vectors from the local vectors via the Karhunen-Loéve (K-L) transformation for those voxels within the extracted region. We choose those first few principal components that stun up to, at least, 90% percent of the total variance for optimizing the dimensions of the feature vectors. Then a modified self-adaptive online vector quantization algorithm is applied to these feature vectors for classification. The presented algorithm requires no prior knowledge of the data distribution except a maximum number of distinct groups for classification, which can be set based on anatomical knowledge. Numerical analysis of the algorithm and experimental tests on brain MR images are presented. Results demonstrate efficient, robust, and self-adaptive properties of the presented algorithm.