24 February 2017 Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization
Author Affiliations +
Abstract
Medical image registration establishes a correspondence between images of biological structures and it is at the core of many applications. Commonly used deformable image registration methods are dependent on a good preregistration initialization. The initialization can be performed by localizing homologous landmarks and calculating a point-based transformation between the images. The selection of landmarks is however important. In this work, we present a learning-based method to automatically find a set of robust landmarks in 3D MR image volumes of the head to initialize non-rigid transformations. To validate our method, these selected landmarks are localized in unknown image volumes and they are used to compute a smoothing thin-plate splines transformation that registers the atlas to the volumes. The transformed atlas image is then used as the preregistration initialization of an intensity-based non-rigid registration algorithm. We show that the registration accuracy of this algorithm is statistically significantly improved when using the presented registration initialization over a standard intensity-based affine registration.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jianing Wang, Jianing Wang, Yuan Liu, Yuan Liu, Jack H. Noble, Jack H. Noble, Benoit M. Dawant, Benoit M. Dawant, } "Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332M (24 February 2017); doi: 10.1117/12.2254769; https://doi.org/10.1117/12.2254769
PROCEEDINGS
13 PAGES


SHARE
Back to Top