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Convergence Performance of Information Theoretic Similarity Measures for Robust Matching
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Konvergens Prestandan för Informationsteoretiska Likhetsmått för Robust Matchning (Swedish)
Abstract [en]

Image matching is an area of significant use in the medical field, as there is a need to match images captured with different modalities, to overcome the limitation that can occur when dealing with individual modalities. However, performing matching of multimodal images may not be a trivial task. Multimodality entails changes in brightness and contrast that might be an obstacle when performing a match using similarity measures.

This study investigated the convergence performance of information-theoretic similarity measures. The similarity measures analysed in this study are mutual information (MI), the cross-cumulative residual entropy (CCRE), and the sum of conditional variances (SCV). To analyse the convergence performance of these measures, an experiment was conducted on one data set introducing the concept of multimodality, and two single images displaying a significant variation in texture. This was to investigate the impact of multimodality and variations in texture on the convergence performance of similarity measures. The experiment investigated the ability for similarity measures to find convergence on MRI and CT medical images after a displacement has occurred.

The results of the experiment showed that the convergence performance of similarity measures varies depending on the texture on images. MI is best suitable in the context of high-textured images while CCRE is more applicable in low-textured images. The measure SCV is the most stable similarity measure as it is little affected by the variation in texture. The experiment also reveals that the convergence performance of the similarity measures identified in the case of unimodality, can be preserved in the context of multimodality.

This study gives better awareness of the convergence performance of similarity measures. This could improve the use of similarity measures in the medical field which could yield better diagnosis of patients’ conditions.

Abstract [sv]

Matching av bilder har en stor betydelse inom det medicinska området. Detta eftersom det finns ett behov av att matcha bilder som tagits med olika modaliteter för att övervinna begränsningarna som kan uppkomma när man arbetar med en modalitet. Dock är det inte alltid trivialt att utföra matchning av multimodala bilder. Multimodalitet medför förändringar i ljusstyrka och kontrast som kan vara ett hinder vid utförande av matchning med likhetsmått.

                                                         

Denna rapport undersökte konvergens prestanda av informationsteoretiska likhetsmått. Likhetsmåtten som behandlades i denna studie var mutual information (MI), cross­cumulative residual entropy (CCRE), och the sum of conditional variances (SCV). För att analysera konvergens perstandat av likhesmåtten, utfördes ett experiment på en dataset som introducerar multimodalitet, och två enskilda bilder som har en betydande variation i struktur. Detta för att undersöka om konvergens av likhetsmåttena påverkas av multimodalitet och variation i struktur. Experimentet fokuserades till att undersöka möjligheten för de olika likhetsmåtten att hitta konvergens på MRI och CT medicinsiska bilder, efter att en manipulering av deras positioner har skett.

                                                         

Resultaten av denna studie visade att konvergens prestanda av likhetsmåtten varierade beroende på mängd struktur i datat. MI visade sig vara bäst lämpad i samband med tydlig struktur medan CCRE var mer lämplig när strukturen var mindre. SCV visade sig vara det mest stabila likhetsmåttet eftersom det inte påverkades märkbart av struktur variation. Studien visade också att konvergens beteendet av likhetsmåtten som identifierades i samband med unimodalitet, kunde bevaras i multimodalitet.

                                                         

Denna studie ger ökad medvetenhet om konvergens prestanda hos likhetsmåtten. Detta kan leda till en förbättrad användning av likhetsmåtten inom det medicinska området vilket kan ge möjligheter till att ställa bättre diagnoser av patienter.

Place, publisher, year, edition, pages
2016.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-186525OAI: oai:DiVA.org:kth-186525DiVA, id: diva2:927557
Supervisors
Examiners
Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2018-01-10Bibliographically approved

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