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Segmentation, co-registration, and correlation of optical coherence tomography and X-ray images for breast cancer diagnostics

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Title: Segmentation, co-registration, and correlation of optical coherence tomography and X-ray images for breast cancer diagnostics
Author(s): Sun, Jonathan
Advisor(s): Boppart, Stephen
Department / Program: Bioengineering
Discipline: Bioengineering
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: M.S.
Genre: Thesis
Subject(s): x-ray Optical coherence tomography (OCT) automated segmentation
Abstract: In this thesis, x-ray imaging is evaluated and compared with optical coherence tomography (OCT). It is shown that each individual modality provides information about a sample through different physical characteristics. The preclinical variant of x-ray imaging is explored in the context of breast cancer detection and diagnosis because their clinical use could be augmented by OCT. After comparing them, each modality showed its specialized usage. Micro-CT is useful in detecting microcalcifications, the most apparent target of the screening mammogram. OCT can be utilized in the specific cases where high resolution is desired and penetration depth does not need to be over a few millimeters. In the case of a needle probe, OCT could provide images of the vicinity of the probe tip, allowing for better localization and identification of abnormal tissue. In the intraoperative case, OCT can be utilized to evaluate tumor margins and lymph nodes that have been resected in order to decrease the chance of tumor reoccurrence and the need for additional operations. In all types of tumor surgeries, the evaluation of lymph nodes is important to help stage the cancer, and real time feedback of lymph node status could prove to be extremely helpful to the surgeon. OCT, with its scattering based image formation, can provide morphological data about a sample that can augment a surgeons screening and diagnosing abilities when used in conjunction with more traditional imaging modalities. Furthermore, an automated segmentation algorithm would be able to identify tumor areas within the operating room, allowing the surgeon to isolate and remove all tumor tissue.
Issue Date: 2012-09-18
URI: http://hdl.handle.net/2142/34372
Rights Information: Copyright 2012 Jonathan Sun
Date Available in IDEALS: 2012-09-18
Date Deposited: 2012-08
 

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