Abstract：
:Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based on singular values of difference image in mono-modal imaging. In fact, image registration and distortion correction are performed simultaneously in the proposed model. Based on our experiments, the RRSSD similarity measure achieves clinically acceptable registration results, and outperforms other state-of-the-art similarity measures, such as the well-known method of residual complexity.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Ghaffari A,Fatemizadeh Edoi
10.1109/TMI.2017.2744663subject
Has Abstractpub_date
2018-01-01 00:00:00pages
138-150issue
1eissn
0278-0062issn
1558-254Xjournal_volume
37pub_type
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