Deep SAE Feature Learning Based Segmentation for Digital Human Brain Image

There are few algorithms for segmenting cryosection brain images, and most existing segmentation techniques presented limited precision and low efficiency. To address these problems, this paper proposed a novel deep feature learning-based segmentation algorithm using sparse autoencoder（SAE）. At the stage of feature extraction, SAE is trained twice to enhance the discriminability of the deep-learned feature representations. At the stage of classification, a softmax classifier is used for segmenting different objects. Experimental results of white matter segmentation on the Chinese Visible Human（CVH） dataset and its 3-D reconstruction show that, the learned deep feature performs much better in discriminability compared with other representative hand-crafted features（such as intensity, histogram of oriented gradient and principal components analysis） and achieves higher recognition accuracy.