Reducing non-realistic deformations in registration using precise and reliable landmark correspondences. (December 2019)

• Record Type: Journal Article
• Authors: Cai, Naxin; Chen, Houjin; Li, Yanfeng; Peng, Yahui; Li, Jupeng; Li, Xinchun
• Is Part Of: Computers in biology and medicine. Volume 115(2019)
• Publication Date: 2019-12
• Subjects: Image registration -- Non-realistic deformations -- Landmark correspondences -- Local rigid -- Lung DCE-MRI; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103515
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Non-rigid image registration is prone to non-realistic deformations. In this paper, we proposed a novel landmark-correspondence detection algorithm, with which, the non-realistic deformations in image registration can be reduced. Our method consists of the following steps. First, the landmarks in the reference image are extracted by a corner detector. Then the landmarks are transferred to the template image by the proposed Multiscale Local Rigid Matching (MsLRM) algorithm. A two-stage method is designed for outlier removal before the landmark correspondences are incorporated into a FFD-based registration through a penalty term considering that the interpolating splines in FFD are highly sensitive to outliers. The proposed method was validated on both simulated images and real-world clinical lung dynamic contrast-enhanced magnetic resonance images. The results showed that the proposed MsLRM achieved sub-pixel accuracy, and was robust to local contrast changes. On clinical datasets, the MsLRM-based landmark-constrained registration improved the registration accuracy by at least 25%, compared with the state-of-the-art registration methods. It achieved an average expert landmark distance of 0.23 mm, close to the inter-observer variability of 0.17 mm. We conclude that our novel landmark-constrained registration improves registration performance on dynamic medical images and outperforms the state-of-the-art registration methods. Highlights: MsLRM was proposed for searching landmark pairs on dynamic medical images. A two-stage-based method was designed for removing outliers in MsLRM. Our landmark-constrained FFD improved registration accuracy on lung DCE-MRI images.