Robust anatomical landmark detection for MR brain image registration.

Dong Han, Yaozong Gao, Guorong Wu, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

Correspondence matching between MR brain images is often challenging due to large inter-subject structural variability. In this paper, we propose a novel landmark detection method for robust establishment of correspondences between subjects. Specifically, we first annotate distinctive landmarks in the training images. Then, we use regression forest to simultaneously learn (1) the optimal set of features to best characterize each landmark and (2) the non-linear mappings from local patch appearances of image points to their displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Since landmark detection is performed in the entire image domain, our method can cope with large anatomical variations among subjects. We evaluated our method by applying it to MR brain image registration. Experimental results indicate that by combining our method with existing registration method, obvious improvement in registration accuracy can be achieved.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages186-193
Number of pages8
Volume17
EditionPt 1
Publication statusPublished - 2014 Jan 1

ASJC Scopus subject areas

  • Medicine(all)

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  • Cite this

    Han, D., Gao, Y., Wu, G., Yap, P. T., & Shen, D. (2014). Robust anatomical landmark detection for MR brain image registration. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 17, pp. 186-193)