Please use this identifier to cite or link to this item: http://hdl.handle.net/2307/40722
Title: PROCESSING AND QUANTITATIVE CHARACTERIZATION OF SKIN TISSUES USING OPTICAL COHERENCE TOMOGRAPHY AND ON DIAGNOSTIC APPLICATIONS OF ELECTROMAGNETIC SCATTERING
Authors: Adabi, Saba
Advisor: Conforto, Silvia
Pajewski, Lara
Keywords: OCT
SPECKLE
TISSUE CLASSIFICATION
SCATTERING
Issue Date: 24-May-2017
Publisher: Università degli studi Roma Tre
Abstract: Optical Coherence Tomography (OCT) is a non-invasive interferometry based technique that deals with many clinical applications including dermatology. Speckle noise in OCT images, degrades the image quality and makes the edges difficult to be resolved in the process of image reconstruction. Methods – both software and hardware – were developed to mitigate the speckle phenomenon in OCT images. Software based methods are post-processing techniques having the substantial privilege of being applicable in clinical environment where the hardware manipulation is impossible. In this thesis two different software-based speckle reduction methods are developed for OCT images; an adaptive cluster-based Wiener filter de-noising algorithm considering the architectural structure of skin layers, and an artificial neural network-based one referring to the statistical distribution of the speckle noise. Furthermore, a universal feature-based de-speckling framework using the inherent characteristics of the OCT image to select optimal filter, is demonstrated. Furthermore, to overcome this spatially variant blurriness problem, an iterative de-convolution total variation method is developed. There is a critical need to systematically analyze OCT images of different sites and identify their significant qualitative and quantitative differences. Therefore, referring to structural OCT and intensity based data together with features dependent classification algorithms, a successful skin model has been developed via normal and cancerous classification. Prior to skin tissue characterization analysis, a skin layer detection algorithm based on graph theory for OCT images of skin is developed. The results of tissue analysis can be extended and added to OCT machine as a kernel for a less subjective cancer diagnosis. In section2, this thesis deals with electromagnetic forward and inverse scattering problems for objects in a host medium, Finite Difference Time Domain (FDTD) modeling and signal processing associated with it.
URI: http://hdl.handle.net/2307/40722
Access Rights: info:eu-repo/semantics/openAccess
Appears in Collections:X_Dipartimento di Ingegneria
T - Tesi di dottorato

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