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Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation

Jennifer Alvén (Institutionen för signaler och system, Bildanalys och datorseende) ; Alexander Norlén (Institutionen för signaler och system, Bildanalys och datorseende) ; Olof Enqvist (Institutionen för signaler och system, Bildanalys och datorseende) ; Fredrik Kahl (Institutionen för signaler och system, Bildanalys och datorseende)
Scandinavian Conference on Image Analysis Proceedings (Lecture Notes in Computer Science) Vol. 9127 (2015), p. 92-102.
[Konferensbidrag, refereegranskat]

Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensitybased registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based registration method for affine registration is presented. The algorithm constructs an abstract representation of the entire atlas set, an uberatlas, through clustering of features that are similar and detected consistently through the atlas set. This is done offline. At runtime only the feature clusters are matched to the target image, simultaneously yielding robust correspondences to all atlases in the atlas set from which the affine transformations can be estimated efficiently. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding gold standards. Our approach succeeds in producing better and more robust segmentation results compared to two baseline methods, one intensity-based and one feature-based, and significantly reduces the running times.

Denna post skapades 2015-05-25. Senast ändrad 2016-06-03.
CPL Pubid: 217526


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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)


Medicinsk bildbehandling

Chalmers infrastruktur