Automated fuzzy logic based skull stripping in neonatal and infantile MR images

Automated morphometric analysis using human brain magnetic resonance (MR) images is an effective approach to investigate the morphological changes of the brain. However, even though many methods for adult brain have been studied, there are few studies for infantile brain. Same as the adult brain, it is effective to measure cerebral surface and for quantitative diagnosis of neonatal and infantile brain diseases. This article proposes a skull stripping method that can be applied to the neonatal and infantile brain. The proposed method can be applied to both of T1 weighted and T2 weighted MR images. First, the proposed method estimates intensity distribution of white matter, gray matter, cerebrospinal fluid, fat, and others using a priori knowledge based Bayesian classification with Gaussian mixture model. The priori knowledge is embedded by representing them with fuzzy membership functions. Second, the proposed method optimizes the whole brain by using fuzzy active surface model, which evaluates the deforming model with fuzzy rules. The proposed method was applied to 26 neonatal and infantile subjects between -4 weeks and 4 years 1 month old. The results showed that the proposed method stripped skull well from any neonatal and infantile MR images.