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Joint-Saliency Structure Adaptive Kernel Regression with Adaptive-Scale Kernels for Deformable Registration of Challenging Images

Binjie Qin,Zhuangming Shen,Zeshan Fu,Zien Zhou,Yisong Lv,Jinsong Bao

Published 2018 in IEEE Access

ABSTRACT

This paper proposes a locally adaptive kernel regression with adaptive-scale kernels for deformable image registration with outliers (i.e., missing correspondences and large local deformations). The adaptive kernel regression locally constructs dense deformation fields from the weighted contributions of each pixel’s surrounding discrete displacement fields in a moving anisotropic kernel by exploiting the contextual deformations of the corresponding saliency structures in the two images. Specifically, we first propose an effective superpixel-based structure scale estimator to estimate the boundary-aware structure scale of each reference structure. We further propose an edge-aware mismatch scale measuring the mismatch degree of the edge structures to be matched in the images. By combining the boundary-aware structure scale with the edge-aware mismatch scale of the underlying saliency structures to be matched, we define edge-aware adaptive-scale kernels for the locally adaptive kernel regression to efficiently construct deformations for deformable registration with outliers. The experiments show that the proposed method achieves not only state-of-the-art matching accuracy for normal corresponding structures but also the best matching efficiency for outlier structures in deformable image registration.

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