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1	The characterization of retinal nerve fiber layer thickness in normal,high-tension and normal-tension glaucoma using optical coherencetomography Mok, Kwok-hei., 莫國熙. January 2005 (has links) published_or_final_version / abstract / Anatomy / Doctoral / Doctor of Philosophy Coherence (Optics) Retina - Tomography. Glaucoma - Tomography. Optical tomography. Glaucoma - Diagnosis.
2	Segmentation of human retinal layers from optical coherence tomography scans Hammes, Nathan M. 09 February 2015 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / An algorithm was developed in to eﬃciently segment the inner-limiting membrane (ILM) and retinal pigmented epithelium (RPE) from spectral domain-optical coherence tomography image volumes. A deformable model framework is described and implemented in which free-form deformations (FFD) are used to direct two deformable sheets to the two high-contrast layers of interest. Improved accuracy in determining retinal thickness (the distance between the ILM and the RPE) is demonstrated against the commercial state-of-the-art Spectralis OCT native segmentation software. A novel adaptation of the highest conﬁdence ﬁrst (HCF) algorithm is utilized to improve upon the initial results. The proposed adaptation of HCF provides distance-based clique potentials and an eﬃcient solution to layer-based segmentation, reducing a 3D problem to 2D inference. Optical coherence tomography Deformable models Image processing Surface segmentation Highest confidence first Retina - Tomography Eye - Tomography Optical coherence tomography
3	Real-time adaptive-optics optical coherence tomography (AOOCT) image reconstruction on a GPU Shafer, Brandon Andrew January 2014 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Adaptive-optics optical coherence tomography (AOOCT) is a technology that has been rapidly advancing in recent years and offers amazing capabilities in scanning the human eye in vivo. In order to bring the ultra-high resolution capabilities to clinical use, however, newer technology needs to be used in the image reconstruction process. General purpose computation on graphics processing units is one such way that this computationally intensive reconstruction can be performed in a desktop computer in real-time. This work shows the process of AOOCT image reconstruction, the basics of how to use NVIDIA's CUDA to write parallel code, and a new AOOCT image reconstruction technology implemented using NVIDIA's CUDA. The results of this work demonstrate that image reconstruction can be done in real-time with high accuracy using a GPU. Adaptive-optics GPU Parallel Processing OCT Optical Coherence Tomography Image Reconstruction Optics, Adaptive -- Research -- Analysis Imaging systems in medicine Holography in medicine Coherence (Optics) Image processing Image reconstruction Real-time programming Eye -- Tomography Retina -- Tomography Graphics processing units -- Programming
4	A scalable approach to processing adaptive optics optical coherence tomography data from multiple sensors using multiple graphics processing units Kriske, Jeffery Edward, Jr. 12 1900 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Adaptive optics-optical coherence tomography (AO-OCT) is a non-invasive method of imaging the human retina in vivo. It can be used to visualize microscopic structures, making it incredibly useful for the early detection and diagnosis of retinal disease. The research group at Indiana University has a novel multi-camera AO-OCT system capable of 1 MHz acquisition rates. Until this point, a method has not existed to process data from such a novel system quickly and accurately enough on a CPU, a GPU, or one that can scale to multiple GPUs automatically in an efficient manner. This is a barrier to using a MHz AO-OCT system in a clinical environment. A novel approach to processing AO-OCT data from the unique multi-camera optics system is tested on multiple graphics processing units (GPUs) in parallel with one, two, and four camera combinations. The design and results demonstrate a scalable, reusable, extensible method of computing AO-OCT output. This approach can either achieve real time results with an AO-OCT system capable of 1 MHz acquisition rates or be scaled to a higher accuracy mode with a fast Fourier transform of 16,384 complex values. OCT GPU Retina -- Tomography -- Research Diagnostic imaging -- Research Imaging systems in medicine Graphics processing units -- Programming Optical pattern recognition High performance computing -- Research Parallel algorithms -- Research

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