Segmentation of head MR images using hybrid neural networks of unsupervised learning

This paper presents an unsupervised segmentation method using hybridized Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART) based only on the brightness distribution and characteristics of head MR images. We specifically examine the features of mapping while maintaining topological relations of weights with SOMs and while integrating a suitable number of categories with Fuzzy ART. Our method can extract intracranial regions using Level Set Methods (LSMs) of deformable models from head MR images. For the extracted intracranial regions, our method segments brain tissues with high granularity using SOMs. Subsequently, these regions are integrated with Fuzzy ART while maintaining relations of anatomical structures of brain tissues and the order of brightness on T2-weighted images. We applied our method to head MR images that are used at clinical sites. We obtained effective and objective segmentation results according to the anatomical structural information of the brain for supporting diagnosis of brain atrophy. Moreover, we applied our method to a head MR image database including data of 30 men and women in their 30s-70s. Results revealed a significant correlation between aging and expanding of cerebrospinal fluid (CSF).