Graph-based groupwise image registration (G-GIR) aims to register a group of input images accurately without any bias. In G-GIR, an image similarity metric (ISM) is used to construct a graph that links similar images with graph edges. From the graph, a group center image and the shortest paths linking it to all other images can be determined. The deformation field aligning each image to the group center image can be obtained by composing sub-deformation fields that come from registration of adjacent images along the corresponding shortest path. The majority of ISMs used in G-GIR are based on image intensity. Since image intensity can be ambiguous and is not directly related to deformation directions, inconsistency problem in the sub-deformation fields along the shortest paths can occur. The word ''inconsistency'' mentioned here refers to the directions of deformation vectors in the sub-deformation fields along each shortest path are significantly different or even opposite at corresponding locations. Such problem can make G-GIR inefficient and easily to be trapped in local minimum. In this paper, we propose a new ISM for G-GIR, by which the consistency in the sub-deformation fields along the shortest paths can be significantly improved. We evaluate our method in comparison with three state-of-The-Art ISMs using a common G-GIR framework. The experimental results with both toy and real images show that our method significantly improves registration accuracy.
Bibliographical noteFunding Information:
Manuscript received October 28, 2018; accepted December 2, 2018. Date of publication December 6, 2018; date of current version July 17, 2019. This work was supported in part by the NIH Grants AG053867, EB006733, and EB008374 and in part by the National Natural Science Foundation of China under Grant 61502002. (Corresponding author: Dinggang Shen.) Z. Tang is with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill and also with the School of Computer Science and Technology, Anhui University.
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- MR brain images
- groupwise image registration
- image similarity metric
ASJC Scopus subject areas
- Biomedical Engineering