Image super-resolution by supervised adaption of patchwise self-similarity from high-resolution image

Guorong Wu, Xiaofeng Zhu, Qian Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Image super-resolution is of great interest in medical imaging field. However, different from natural images studied in computer vision field, the low-resolution (LR) medical imaging data is often a stack of high-resolution (HR) 2D slices with large slice thickness. Consequently, the goal of superresolution for medical imaging data is to reconstruct the missing slice(s) between any two consecutive slices. Since some modalities (e.g., T1-weighted MR image) are often acquired with high-resolution (HR) image, it is intuitive to harness the prior self-similarity information in the HR image for guiding the super-resolution of LR image (e.g., T2-weighted MR image). The conventional way is to find the profile of patchwise self-similarity in the HR image and then use it to reconstruct the missing information at the same location of LR image. However, the local morphological patterns could vary significantly across the LR and HR images, due to the use of different imaging protocols. Therefore, such direct (un-supervised) adaption of self-similarity profile from HR image is often not effective in revealing the actual information in the LR image. To this end, we propose to employ the existing image information in the LR image to supervise the estimation of self-similarity profile by requiring it not only being optimal in representing patches in the HR image, but also producing less reconstruction errors for the existing image information in the LR image. Moreover, to make the anatomical structures spatially consistent in the reconstructed image, we simultaneously estimate the self-similarity profiles for a stack of patches across consecutive slices by solving a group sparse patch representation problem. We have evaluated our proposed super-resolution method on both simulated brain MR images and real patient images with multiple sclerosis lesion, achieving promising results with more anatomical details and sharpness.

Original languageEnglish
Title of host publicationPatch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
EditorsPierrick Coupé, Brent Munsell, Guorong Wu, Yiqiang Zhan, Daniel Rueckert
PublisherSpringer Verlag
Pages10-18
Number of pages9
ISBN (Print)9783319281933
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9467
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
CountryGermany
CityMunich
Period15/10/915/10/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Image super-resolution by supervised adaption of patchwise self-similarity from high-resolution image'. Together they form a unique fingerprint.

  • Cite this

    Wu, G., Zhu, X., Wang, Q., & Shen, D. (2015). Image super-resolution by supervised adaption of patchwise self-similarity from high-resolution image. In P. Coupé, B. Munsell, G. Wu, Y. Zhan, & D. Rueckert (Eds.), Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 10-18). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467). Springer Verlag. https://doi.org/10.1007/978-3-319-28194-0_2