Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies

Image segmentation consists in partitioning an image into different regions. MRI image segmentation is especially interesting, since an accurate segmentation of the different brain tissues provides a way to identify many brain disorders such as dementia, schizophrenia or even the Alzheimer’s disease. A large variety of image segmentation approaches have been implemented before. Nevertheless, most of them use a priori knowledge about the voxel classification, which prevents figuring out other tissue classes different from the classes the system was trained for. This paper presents two unsupervised approaches for brain image segmentation. The first one is based on the use of relevant information extracted from the whole volume histogram which is processed by using self-organizing maps (SOM). This approach is faster and computationally more efficient than previously reported methods. The second method proposed consists of four stages including MRI brain image acquisition, first and second order feature extraction using overlapping windows, evolutionary computing-based feature selection and finally, map units are grouped by means of a novel SOM clustering algorithm. While the first method is a fast procedure for the segmentation of the whole volume and provides a way to model tissue classes, the second approach is a more robust scheme under noisy or bad intensity normalization conditions that provides better results using high resolution images, outperforming the results provided by other algorithms in the state-of-theart, in terms of the average overlap metric. The proposed algorithms have been successfully evaluated using the IBSR and IBSR 2.0 databases, as well as high-resolution MR images from the Nuclear Medicine Department of the “Virgen de las Nieves” Hospital, Granada, Spain (VNH), providing in any case good segmentation results.