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Automatic landmark identification in digital images of Drosophila wings for improved morphometric analysis
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]

A considerable number of morphometric studies which are performed nowadays with fly wing images require manual annotation of landmarks or key-points. This work is tedious and time consuming for researchers. This is why there is interest in automating the process and therefore several approaches for this purpose have been developed. The problem with these methods is that they are difficult to use and are usually specific to a particular imaging format and species. This project's objective is to develop two methods, one based on classic image analysis techniques, and another one based on machine learning algorithms. A comparison is made to understand the strengths of each approach and find a solution that is general and easy to use. The first method (classic) uses domain knowledge to extract features and match template structures to determine landmark locations. Every parameter is fine-tuned manually and requires a long time to develop. Nevertheless, the results achieve human-level precision. The second method uses deep learning algorithms to train 30 neural networks which divide the image into regions and extract the coordinates of the landmarks directly. The results obtained for the machine learning approach are similar (approximately 10-pixel precision for 2448 x 2048 size images), with the advantage that it does not require any domain knowledge and can be reused for any kind of format and species. A solution that combines the strengths of both methods seems to be the best path to find a fully automatic algorithm.

Place, publisher, year, edition, pages
2019. , p. 72
Series
UPTEC IT, ISSN 1401-5749 ; 19004
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-393388OAI: oai:DiVA.org:uu-393388DiVA, id: diva2:1353065
Educational program
Master Programme in Computational Science
Supervisors
Examiners
Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-09-20Bibliographically approved

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CiteExportLink to record
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  • apa
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