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Non-local means denoising ofprojection images in cone beamcomputed tomography
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A new edge preserving denoising method is used to increase image quality in cone beam computed tomography. The reconstruction algorithm for cone beam computed tomography used by Elekta enhances high frequency image details, e.g. noise, and we propose that denoising is done on the projection images before reconstruction. The denoising method is shown to have a connection with computational statistics and some mathematical improvements to the method are considered. Comparisons are made with the state-of-theart method on both artificial and physical objects. The results show that the smoothness of the images is enhanced at the cost of blurring out image details. Some results show how the setting of the method parameters influence the trade off between smoothness and blurred image details in the images.

Abstract [sv]

En ny kantbevarande brusreduceringsmetod används för att förbättra bildkvaliteten för digital volymtomografi med konstråle. Rekonstruktionsalgoritmen for digital volymtomografi med konstråle som används av Elekta förstärker högfrekventa bilddetaljer, t.ex. brus, och vi föreslår att brusreduceringen genomförs på projektionsbilderna innan de genomgår rekonstruktion. Den brusreducerande metoden visas ha kopplingar till datorintensiv statistik och några matematiska förbättringar av metoden gås igenom. Jämförelser görs med den bästa metoden på både artificiella och fysiska objekt. Resultaten visar att mjukheten i bilderna förbättras på bekostnad av utsmetade bilddetaljer. Vissa resultat visar hur parametersättningen för metoden påverkar avvägningen mellan mjukhet och utsmetade bilddetaljer i bilderna.

Place, publisher, year, edition, pages
2013. , 80 p.
Series
TRITA-MAT-E, 2013:23
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-122419OAI: oai:DiVA.org:kth-122419DiVA: diva2:626254
External cooperation
Elekta instrument AB
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Uppsok
Physics, Chemistry, Mathematics
Supervisors
Examiners
Available from: 2013-06-07 Created: 2013-05-20 Last updated: 2013-06-07Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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