24 February 2017 Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization
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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.
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Jianing Wang, Yuan Liu, Jack H. Noble, 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
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