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Presentation + Paper
15 February 2021 Inner limiting membrane segmentation and surface visualization method on retinal OCT images
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In recent years, the segmentation and projection techniques of different structures of medical interest have had significant growth due to its usefulness; doctors have been using them as tools for the diagnosis and evolution of different diseases. The segmentation of the Inner Limiting Membrane (ILM) in retinal scans acquired using the Optical Coherence Tomography (OCT) imaging technique has generated particular interest in the medical area since it provides clinically relevant information about diseases such as Glaucoma, Diabetic Macular Edema (DME) or Multiple Sclerosis. Furthermore, the generation of a surface that shows the current morphological situation of the scanned retinal area is a tool that complements the medical analysis. In this paper, a new methodology for the ILM segmentation on OCT retinal images and a surface projection from different axially spaced scans acquired over the macular and peripapillary zone is presented. The proposed scheme consists of a wavelet-based denoising step and a contrast enhancement stage using Eigenvalues of Hessian matrix, while the segmentation process is based on the Canny edge detection algorithm; these stages are applied to each image of a C-scan for a later surface generation using Cubic spline interpolation. This method is applied to a publicly available OCT data-set composed of 22 patients with several retinal diseases obtaining a correct individual segmentation of each image, while the surface generation results demonstrate high performance in the visualization of the ILM morphology, which can be used for dimensional analysis of this membrane.
Conference Presentation
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Oscar Ramos-Soto, Erick Rodríguez-Esparza, Marco Pérez-Cisneros, and Sandra E. Balderas-Mata "Inner limiting membrane segmentation and surface visualization method on retinal OCT images", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1160016 (15 February 2021);

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