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http://dx.doi.org/10.9717/kmms.2020.24.2.178

MRI Image Super Resolution through Filter Learning Based on Surrounding Gradient Information in 3D Space  

Park, Seongsu (Major of AI., Dept. of Information Convergence Engineering, Pusan National University)
Kim, Yunsoo (Major of AI., Dept. of Information Convergence Engineering, Pusan National University)
Gahm, Jin Kyu (School of Computer Science and Engineering, Pusan National University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.2, 2021 , pp. 178-185 More about this Journal
Abstract
Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.
Keywords
Super Resolution; Image Enhancement; Machine Learning; Medical Image Processing; 3D Image Analysis;
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