Hippocampus segmentation in MR images using sparse patch representation and discriminative dictionary learning

Yonggang Shi, Dongqing Wang, Zhiwen Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

An automatic segmentation method of hippo-campus for volume measurement in MR brain images by using sparse patch representation and discriminative dictionary learning is proposed in this paper, which can overcome the limitation of multi-atlas approaches that mostly rely on similarity between target image and atlases for more accurate segmentation. In the proposed method, atlases are registered to a target image, then patch-based features including intensity and gradient are extracted from registered atlases for a dictionary learning model, from which dictionaries and classifiers are learned simultaneously. The label for voxels in the target image is determined via solving sparse representation of target patch over learned dictionary. Performance of the proposed method is evaluated on 30 subjects from the public dataset CIND by cross validation. Qualitative and quantitative comparisons illustrate that the proposed method is competitive compared with state-of-the-art approaches based on multi-atlas.

Original languageEnglish
Title of host publicationIET Conference Publications
PublisherInstitution of Engineering and Technology
EditionCP680
ISBN (Electronic)9781785610448
Publication statusPublished - 2015
Event2015 IET International Conference on Biomedical Image and Signal Processing, ICBISP 2015 - Beijing, China
Duration: 19 Nov 2015 → …

Publication series

NameIET Conference Publications
NumberCP680
Volume2015

Conference

Conference2015 IET International Conference on Biomedical Image and Signal Processing, ICBISP 2015
Country/TerritoryChina
CityBeijing
Period19/11/15 → …

Keywords

  • Dictionary learning
  • Hippocampus segmentation
  • Patchbased feature
  • Sparse representation

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