Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images
Syoji Kobashi, Jayaram K. Udupa · 2012
Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and fuzzy object models. The proposed method evaluates object affinity and homogeneity using the MR signal, and employs a fuzzy object model, which is built from training datasets. We have evaluated the proposed method based on 10 newborn MR images with subject revised age between -1 month and 2 months. These studies indicate that the use of a fuzzy object model is effective in improving the segmentation accuracy.