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Structural representation models for multi-modal image registration in biomedical applications
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In clinical applications it is often beneficial to use multiple imaging technologies to obtain information about different biomedical aspects of the subject under investigation, and to make best use of such sets of images they need to first be registered or aligned. Registration of multi-modal images is a challenging task and is currently the topic of much research, with new methods being published frequently.

Structural representation models extract underlying features such as edges from images, distilling them into a common format that can be easily compared across different image modalities. This study compares the performance of two recent structural representation models on the task of aligning multi-modal biomedical images, specifically Second Harmonic Generation and Two Photon Excitation Fluorescence Microscopy images collected from skin samples. Performance is also evaluated on Brightfield Microscopy images.

The two models evaluated here are PCANet-based Structural Representations (PSR, Zhu et al. (2018)) and Discriminative Local Derivative Patterns (dLDP, Jiang et al. (2017)). Mutual Information is used to provide a baseline for comparison. Although dLDP in particular gave promising results, worthy of further investigation, neither method outperformed the classic Mutual Information approach, as demonstrated in a series of experiments to register these particularly diverse modalities.

Place, publisher, year, edition, pages
2019. , p. 42
Series
IT ; IT19074
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-410820OAI: oai:DiVA.org:uu-410820DiVA, id: diva2:1431385
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2020-05-20 Created: 2020-05-20 Last updated: 2020-05-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • nn-NB
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Output format
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