High-order boltzmann machine-based unsupervised feature learning for multi-atlas segmentation

Multi-atlas based label fusion methods have been successfully used for medical image segmentation. In the field of brain region segmentation, multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases. Most of existing multi-atlas based methods usually use intensity feature, which is hard to capture high-order information in brain images. In light of this, in this paper, we endeavor to apply high-order restricted Boltzmann machines to represent brain images and use the learnt feature for brain region of interesting (ROIs) segmentation. Specifically, we firstly capture the covariance and the mean information from patches by high-order Boltzmann Machine. Then, we propagate the label by the similarity of the learnt high-order features. We validate our feature learning method on two well-known label fusion methods e.g., local-weighted voting (LWV) and non-local mean patch-based method (PBM). Experimental results on the NIREP dataset demonstrate that our method can improve the performance of both LWV and PBM by using the high-order features.