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Automatic Interpretation of Lung CT Volume Images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Computer-aided systems in medical imaging nowadays play a crucial role to assist clinicians. Research shows that the use of computer-aided systems is indispensable to alleviate the workload of clinicians. In this thesis, a new framework, which interprets lung computed tomography (CT) volume images automatically is proposed to help clinicians. A common interpretation tasks of lung CT volume images involves segmentation and extraction of the organs in the thoracic cavity. The developed framework consists of six main steps to segment and extracts the major parts in the thoracic cavity. In the first step, the region of interest (ROI) was determined for following segmentation and extraction tasks. Then in the second step, the large airways were extracted from the ROI mask. In the third step, the left and the right lungs were segmented from the ROI mask. If the left and the right lungs touched each other, the separation process was performed in the fourth step. After that in the fifth step, mediastinum volume was extracted from the ROI mask. Finally, the vessels tree were segmented from inside of the lungs. The developed framework was tested against 133 computed tomography pulmonary angiography (CTPA) volume images, and good results have been achieved according to the qualitative evaluation (The average success rate of all steps are over 85% which gave satisfying results for all segmentation and extraction tasks). Besides, the average execution time for the developed framework is 83.46 seconds per cases and 0.195 seconds per slices, which were provided low computational cost according to the current studies and manual interpretation made by clinicians

Place, publisher, year, edition, pages
2017. , p. 95
Series
IT ; 17075
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-397449OAI: oai:DiVA.org:uu-397449DiVA, id: diva2:1371624
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2019-11-20Bibliographically approved

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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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