umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Combining assembles of domain expert markings
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2010 (English)Independent thesis Advanced level (degree of Master (One Year)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

Breast cancer is diagnosed in more than 6300 Swedish women every year. Mammograms, which are X-ray images of breasts, are taken as part of a nationwide screening process and are analyzed for anomalies by radiologists. This analysis process could be made more efficient by using computer-aided image analysis to assist quality control of the mammograms. However, the development of such image analysis methods requires what is called a “ground truth”. The ground truth is used as a key in algorithm development and represents the true information in the depicted object. Mammograms are 2D projections of deformed 3D objects, and in these cases the ground truth is almost impossible to procure. Instead a surrogate ground truth is constructed. ALGSII, a novel method for ranking shapes within a given set, was recently developed for measuring the level of agreement among ensembles of markings produced by experts of glandular tissue in mammograms. It was hypothesized in this thesis that the ALGSII measure could be used to construct a surrogate truth based on the markings from domain experts.Markings from segmentations of glandular tissue, performed by 5 different field experts on 162 mammograms, comprised the working data for this thesis project. An algorithm was developed that, given a fixed set of markings, takes an initial shape and modifies it iteratively until it becomes the “optimal shape” - the shape with the highest level of agreement in the group of markings according to the ALGSII measure. The algorithm was optimized with egard to rate of accepted shape changes and computational complexity.The developed algorithm was successful in producing an optimal shape according to the definition of maximizing the ALGSII measure in 100% of the cases tested. The algorithm showed stability for the given data set, and its performance was significantly increased by the implemented optimizations.

Place, publisher, year, edition, pages
2010.
Series
UMNAD, 837
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-34405OAI: oai:DiVA.org:umu-34405DiVA: diva2:321481
External cooperation
Educational program
Master of Science Programme in Computing Science and Engineering
Presentation
(English)
Uppsok
Technology
Supervisors
Examiners
Available from: 2010-06-01 Created: 2010-06-01 Last updated: 2017-01-23Bibliographically approved

Open Access in DiVA

fulltext(3117 kB)208 downloads
File information
File name FULLTEXT01.pdfFile size 3117 kBChecksum SHA-512
8d9bf5f22f7ca531581f744dfd9269549e861b6fff2756c4b66f05a690cdaf1575f703ec847540185b294518034359a30b130f7fbc4c9f0b4f95742c20af1040
Type fulltextMimetype application/pdf

By organisation
Department of Computing Science
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 208 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 111 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf