OCT Image Processing

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dc.contributor.advisorHarangi, Balázs
dc.contributor.authorMaleki, Mojtaba
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-02-22T22:46:43Z
dc.date.available2025-02-22T22:46:43Z
dc.date.created2024-11-04
dc.description.abstractThis bachelor thesis aims to enhance the quality, alignment, and interpretability of Optical Coherence Tomography (OCT) images, a vital tool in diagnosing eye diseases such as glaucoma and macular degeneration. Despite OCT’s high-resolution capabilities, challenges like image misalignment and data gaps reduce its effectiveness in clinical settings. This research develops advanced preprocessing, alignment, and interpolation techniques, supported by machine learning, to improve OCT image accuracy and reliability. Beyond technical advancements, this study underscores the clinical impact, equipping ophthalmologists with more precise diagnostic tools and facilitating long-term patient monitoring. Additionally, these methodologies have potential applications in other medical imaging fields, encouraging interdisciplinary innovation.
dc.description.courseProgramtervező informatikus
dc.description.degreeBSc/BA
dc.format.extent61
dc.identifier.urihttps://hdl.handle.net/2437/387461
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectOptical Coherence Tomography
dc.subjectImage Preprocessing
dc.subjectFuture Directions in OCT
dc.subjectPatient-centered Imaging Techniques
dc.subjectMedical Applications
dc.subject.dspaceInformatics::Computer Science
dc.subject.dspaceBiology::Human Biolology
dc.titleOCT Image Processing
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