Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
Paper i proceeding, 2015

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.

Författare

Jennifer Alvén

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Alexander Norlén

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Olof Enqvist

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Fredrik Kahl

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 9127 92-102

Ämneskategorier

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling

DOI

10.1007/978-3-319-19665-7_8

ISBN

978-3-319-19664-0

Mer information

Skapat

2017-10-07