Analysis of Optical Coherence Tomography Images by Dictionary Learning Methods

Razavi, Raha

dc.contributor.advisor	Plonka-Hoch, Gerlind Prof. Dr.
dc.contributor.author	Razavi, Raha
dc.date.accessioned	2024-07-17T08:42:10Z
dc.date.available	2024-07-24T00:50:07Z
dc.date.issued	2024-07-17
dc.identifier.uri	http://resolver.sub.uni-goettingen.de/purl?ediss-11858/15373
dc.identifier.uri	http://dx.doi.org/10.53846/goediss-10616
dc.format.extent	180	de
dc.language.iso	eng	de
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject.ddc	510	de
dc.title	Analysis of Optical Coherence Tomography Images by Dictionary Learning Methods	de
dc.type	doctoralThesis	de
dc.contributor.referee	Plonka-Hoch, Gerlind Prof. Dr.
dc.date.examination	2024-07-08	de
dc.description.abstracteng	An accurate analysis of Optical Coherence Tomography (OCT) images plays an important role in the diagnosis of abnormalities within these images. Unfortunately, noise caused by various sources degenerates the quality of the OCT images. Among all, OCT images suffer mainly from speckle noise caused by the scattering of light waves in the physical device. In this thesis, we propose new denoising methods for OCT images and investigate the noise removal impact on segmentation and classification results. We consider sparse representation modeling and aim to benefit from non-data-adaptive multi-scale ($X$-let) transforms. We study these transforms and compare their performances for OCT images. These transforms, besides their stable performance, are also useful for the analysis of different types of OCT images. We employ and adapt decomposition algorithms for OCT images with noise reduction applications within the transform domain of $X$-let transforms. The obtained components from these methods are distinct in texture, piecewise smooth parts, and singularities along curves. Numerically and visually, we achieve strongly enhanced quality and denoised OCT images by applying adaptive local thresholding techniques separately to each image component. The denoising performance outperforms other state-of-the-art denoising algorithms regarding the different image quality measures. Lastly, we emphasize and demonstrate the significance of denoising as the preprocessing step for segmentation and classification results. The acquired decomposition of OCT images into well-interpretable (denoised) image components using our proposed algorithms can be exploited further for image processing tasks.	de
dc.contributor.coReferee	Russell, Luke Prof. Dr.
dc.subject.eng	Optical Coherence Tomography	de
dc.subject.eng	OCT	de
dc.subject.eng	Denoising	de
dc.subject.eng	Image Processing	de
dc.subject.eng	Medical Image Processing	de
dc.subject.eng	Non-data-adaptive Transforms	de
dc.identifier.urn	urn:nbn:de:gbv:7-ediss-15373-3
dc.affiliation.institute	Fakultät für Mathematik und Informatik	de
dc.subject.gokfull	Mathematics (PPN61756535X)	de
dc.description.embargoed	2024-07-24	de
dc.identifier.ppn	1895791308
dc.identifier.orcid	https://orcid.org/0009-0002-9624-7421	de
dc.notes.confirmationsent	Confirmation sent 2024-07-17T08:45:01	de

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