, Volume 16, Issue 3–4, pp 411–423 | Cite as

Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation

  • Yan Wang
  • Guangkai Ma
  • Xi Wu
  • Jiliu Zhou
Original Article


Automatic and accurate segmentation of hippocampal structures in medical images is of great importance in neuroscience studies. In multi-atlas based segmentation methods, to alleviate the misalignment when registering atlases to the target image, patch-based methods have been widely studied to improve the performance of label fusion. However, weights assigned to the fused labels are usually computed based on predefined features (e.g. image intensities), thus being not necessarily optimal. Due to the lack of discriminating features, the original feature space defined by image intensities may limit the description accuracy. To solve this problem, we propose a patch-based label fusion with structured discriminant embedding method to automatically segment the hippocampal structure from the target image in a voxel-wise manner. Specifically, multi-scale intensity features and texture features are first extracted from the image patch for feature representation. Margin fisher analysis (MFA) is then applied to the neighboring samples in the atlases for the target voxel, in order to learn a subspace in which the distance between intra-class samples is minimized and the distance between inter-class samples is simultaneously maximized. Finally, the k-nearest neighbor (kNN) classifier is employed in the learned subspace to determine the final label for the target voxel. In the experiments, we evaluate our proposed method by conducting hippocampus segmentation using the ADNI dataset. Both the qualitative and quantitative results show that our method outperforms the conventional multi-atlas based segmentation methods.


Multi-atlas based method Margin fisher analysis Structured discriminant embedding Subspace learning Patch-based label fusion 



This work is supported in part by NSFC project 61701324, Sience&Technology Department of Sichuan Province 2016JZ0014, Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201715).

Compliance with Ethical Standards

Conflict of Interest

The authors declare no conflict of interest.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer ScienceSichuan UniversityChengduChina
  2. 2.Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)FuzhouChina
  3. 3.Space Control and Inertial Technology Research CenterHarbin Institute of TechnologyHarbinChina
  4. 4.Department of Computer ScienceChengdu University of Information TechnologyChengduChina

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