Medical image processing and signal analysis of optical coherence tomography (OCT) image segmentation

Abstract

Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA) are non-invasive imaging techniques that provide high-resolution and detailed images of biological tissues. These imaging modalities are widely utilized in ophthalmology for the diagnosis and treatment of a variety of retinal diseases, including diabetic retinopathy, macular degeneration, and glaucoma. However, interpreting OCT and OCTA images can be challenging due to the complex and heterogeneous nature of biological tissues. Image segmentation is essential to the analysis of OCT and OCTA images because it enables the identification and delineation of relevant structures or regions of interest. The automatic detection and interpretation of OCT images with subcellular resolution will make it possible to diagnose disease in real-time and facilitate the clinical applications of this technology. The project aims to develop new automated and efficient segmentation algorithms on MATLAB to improve the current segmentation techniques for clinical use.