Patch-Based Hippocampus Segmentation Using a Local Subspace Learning Method

Patch-based segmentation methods utilizing multiple atlases have been widely studied for alleviating some misalignments when registering atlases to the target image. However, weights assigned to the fused labels are typically computed based on predefined features (e.g. simple patch intensities), thus being not necessarily optimal. Due to lack of discriminating features for different regions of an anatomical structure, the original feature space defined by image intensities may limit the segmentation accuracy. To address these problems, we propose a novel local subspace learning based patch-wise label propagation method to estimate a voxel-wise segmentation of the target image. Specifically, multi-scale patch intensities and texture features are first extracted from the image patch in order to acquire the abundant appearance information. Then, margin fisher analysis (MFA) is applied to neighboring samples of each voxel to be segmented from the aligned atlases, in order to extract discriminant features. This process can enhance discrimination of features for different local regions in the anatomical structure. Finally, based on extracted discriminant features, the k-nearest neighbor (kNN) classifier is used to determine the final label for the target voxel. Moreover, for the patch-wise label propagation, we first translate label patches into several discrete class labels by using the k-means clustering method, and then apply MFA to ensure that samples with similar label patches achieve a higher similarity and those with dissimilar label patches achieve a lower similarity. To demonstrate segmentation performance, we comprehensively evaluated the proposed method on the ADNI dataset for hippocampus segmentation. Experimental results show that the proposed method outperforms several conventional multi-atlas based segmentation methods.