Neonatal brain segmentation using 4-D fuzzy object model

Brain region segmentation in neonatal magnetic resonance (MR) images is an essential task for computer-aided diagnosis of neonatal brain disorders using MR images. We have proposed a neonatal brain segmentation method using a fuzzy object model (FOM), which represents a prior knowledge of brain shape and location. The FOM is constructed from multiple neonatal brain MR images whose revised age was between 0 and 4 weeks. The method segmented the brain region with a good accuracy for subjects whose age matches of the training data set. To enhance the method, we need multiple FOMs for each age. The other solution is to develop a growable model. This paper introduces 4-D FOM and applies it to neonatal brain segmentation. This paper introduces a neonatal brain segmentation method using 4-D FOM. The proposed method consists of three components. The first part proposes a method for estimating the brain development progress, called growth index in this study, from MR images based on Manifold learning. The second part shows a procedure for generating 4-D FOM using the estimated growth index. The third part is to segment brain region based on fuzzy-connectedness image segmentation using 4-D FOM. The proposed method was applied to 16 neonatal subjects. The results show that 4-D FOM is superior to stable 3-D FOM for segmenting neonatal brain region from MR images.