Abstract
In order to develop a computer-aided diagnosis system for neonatal cerebral disorders, some methods of brain segmentation from MR images using atlas model have been studied. As neonatal cerebrum deforms quickly by natural growth, single model cannot represent growth model properly. Due to the variation of newborn brain growth even at same age, age based model will not give appropriate result. In this paper, we propose a method for estimating growth index using manifold learning and generating fuzzy object growth model (FOGM). Brain anatomical landmarks are used for manifold learning. In addition, we propose a fuzzy connectedness segmentation method using FOGM to segment the brain region. In comparison with the previous single model based method, the proposed method improved the segmentation accuracy by using FOGM.
