Show simple item record

dc.contributor.authorKabiljagic, Dino
dc.date.accessioned2021-03-17 13:20:13 (GMT)
dc.date.available2021-03-17 13:20:13 (GMT)
dc.date.issued2021-03-17
dc.date.submitted2021-03-04
dc.identifier.urihttp://hdl.handle.net/10012/16851
dc.description.abstractMelanoma is the most common type of cancer, and the standard practice used for examining skin lesions is dermoscopy, where dermatologists use an epiluminescence microscope (ELM) to visualize the skin's surface and subsurface structures for anomalies. Conventional ELM instruments are being replaced by digital ELM instruments that enable dermatologists and other health care practitioners to digitally capture, archive, and analyze skin lesions using computer-aided diagnosis (CAD) software. One of the limiting factors of digital ELMs is a trade-off between spatial resolution and field of view (FOV), where a large FOV, which is needed to allow for larger skin lesions to be examined in their entirety, can be achieved by reducing magnification at the cost of spatial resolution (leading to a loss of fine details that can be indicative of malignancy and disease). In this thesis, we introduced the deep computation optics (DCO) framework for the purpose of resolution-enhanced digital ELM to improve the balance between spatial resolution and FOV. More specifically, the multitude of parameters of a deep computational model for numerically magnifying digital ELM images were learned through a wealth of low-resolution and high-resolution digital ELM image pairs. The proposed DCO approaches were experimentally validated, demonstrating improvements in the spatial resolution of the resolution-enhanced digital ELM when compared to more conventional methods, such as bicubic interpolation. Furthermore, we have demonstrated that the spatial resolution-enhancement improvements can be made within the deep computational models themselves where the model's receptive field is of the utmost importance since the missing information is better estimated when there is a larger number of neighbouring pixels involved.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectskin canceren
dc.subjectmelanomaen
dc.subjectepiluminescence microscopyen
dc.subjectdermatologyen
dc.subjectdeep computational opticsen
dc.titleResolution-enhanced Digital Epiluminescence Microscopy Using Deep Computational Opticsen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentSystems Design Engineeringen
uws-etd.degree.disciplineSystem Design Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws-etd.embargo.terms0en
uws.contributor.advisorWong, Alexander
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record


UWSpace

University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages