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Pedro Serranho, Rui Bernardes, Cristina Maduro, Torcato Santos, Jose G. Cunha-Vaz; Synthetic Volume from Real Optical Coherence Tomography Data. Invest. Ophthalmol. Vis. Sci. 2011;52(14):1331.
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To build a mathematical model to mimic a real OCT b-scan/volume without noise, in order to establish a ground truth for image processing performance metrics.
Current image processing techniques (eg. despeckling filtering methods) with application to optical coherence tomography (OCT) rely on the respective qualitative evaluation of its results. Quantitative approaches are reduced to using synthetic images which consists of an homogeneous background and a set of abstract objects, eg. cubes and spheres. In this work, we suggest a mathematical model to address this issue by creating a synthetic b-scan/volume based on any real OCT data scan, that can be used as ground truth for processing methods testing. Eye scans of healthy volunteers and eyes of patients with age-related macular degeneration and diabetic retinopathy were used following Cirrus OCT (Carl Zeiss Meditec, Dublin, CA, USA) scans using both the 200x200x1024 and the 512x128x1024 Macular Cube Protocols. Each of these eye scans was processed in order to extract required parameters. For healthy subjects, only the segmentation of the inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) are needed, though for pathologic eyes, the segmentation of other structures to be preserved might also be needed. In each segmented region, OCT data is surface fitted using appropriate basis functions.
A total of 45 scans were processed resulting in the synthetic data representing the major characteristics of the respective real OCT scans. These results have been used for testing the performance of an improved complex diffusion despeckling method proposed by some of the authors.
This process allows to automatically compute a synthetic OCT scan mimicking a real one. In this way, this process makes it possible to quantify the result of any processing (eg. filtering) by providing adequate synthetic data as ground truth.
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