The ability to detect atherosclerotic plaque from optical coherence tomography (OCT) images by visual inspection is usually limited. We developed a texture based segmentation method using supervised and unsupervised classification to detect atherosclerotic plaque from OCT images without any reliance on visual inspection. Our Supervised method involves extraction of statistical textural features using the Spatial Gray Level Dependence Matrix (SGLDM) method, feature extraction and feature selection method, and application on supervised algorithm (K-nn). Our second method is based on unsupervised classification involves extraction of statistical textural features using the SGLDM method, application of an unsupervised clustering algorithm (K-means) on these features, and mapping of the segmented regions of features back to the actual image. We verified our results by visually comparing them to photographs of the vascular tissue with atherosclerotic plaque that we used to generate our OCT images. Our method could be potentially used in clinical cardiovascular OCT imaging.

The ability to detect atherosclerotic plaque from optical coherence tomography (OCT) images by visual inspection is usually limited. We developed a texture based segmentation method using supervised and unsupervised classification to detect atherosclerotic plaque from OCT images without any reliance on visual inspection. Our Supervised method involves extraction of statistical textural features using the Spatial Gray Level Dependence Matrix (SGLDM) method, feature extraction and feature selection method, and application on supervised algorithm (K-nn). Our second method is based on unsupervised classification involves extraction of statistical textural features using the SGLDM method, application of an unsupervised clustering algorithm (K-means) on these features, and mapping of the segmented regions of features back to the actual image. We verified our results by visually comparing them to photographs of the vascular tissue with atherosclerotic plaque that we used to generate our OCT images. Our method could be potentially used in clinical cardiovascular OCT imaging.

A new optical method, for the assessment of the optic nerve-head topography, was studied theoretically and experimentally. The motivation of this work is in relation with glaucoma, an eye disease that can lead to blindness. Glaucoma manifests itself by a destruction (atrophy) of the retinal axons, which in turn affects the shape (topography) of the optic nerve head (disc). Topographic measurement of the optic nerve head is used for diagnosis and tracking (progression) of glaucoma. The new technique consists of a dual-fringe projection system and a single-view imaging system. The dual-fringe projection system produces a 3-D irradiance distribution in the measurement volume (optic nerve location) and the modulation of the fringes is used to profile the optic nerve head. Since the optic nerve disc is optically accessible only through the ocular media, the new method is conceived to access the optic nerve through the eye pupil and to minimize the effects of ocular variations such as the optical power and third order aberrations of the eye. The method can use a low power broad-band source and is therefore safe for the eye. Two prototypes were built (coherent and incoherent source) and evaluated using an optical system that simulates the eye. The new method can be applied to the topographic measurement of any diffuse object, whether it is directly accessible or not.

Thesis (M.S.)--University of Wyoming, 2006. / Title from PDF title page (viewed on Nov. 14, 2007). Includes bibliographical references (p. 91-92).

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