Segmentation of neurons based on one-class classification

In this paper, we propose a novel one-class classification method to segment neurons. First, a new criterion to select a training set consisting of background voxels is proposed. Then, a discriminant function is learned from the training set that allows determining how similar an unlabeled voxel is to the voxels in the background class. Finally, foreground voxels are assigned as those unlabeled voxels that are not classified as background. Our method was qualitatively and quantitatively evaluated on several dataset to demonstrate its ability to accurately and robustly segment neurons.