Retinal layer segmentation using gradient feature calculation in OCT

Retinal diseases pose significant challenges to global healthcare systems, necessitating accurate and efficient diagnostic methods. Optical Coherence Tomography (OCT) has emerged as a valuable tool for diagnosing and monitoring retinal conditions due to its noncontact and noninvasive nature. This paper presents a novel retinal layering method based on OCT images, aimed at enhancing the accuracy of retinal lesion diagnosis. The method utilizes gradient analysis to effectively identify and segment retinal layers. By selecting a column of pixels as a segmentation line and utilizing gradient information from adjacent pixels, the method initiates and proceeds with the layering process. This approach addresses potential issues arising from partial layer overlapping, minimizing deviations in layer segmentation. Experimental results demonstrate the efficacy of the proposed method in accurately segmenting eight retinal boundaries, with an average absolute position deviation of 1.75 pixels. By providing accurate segmentation of retinal layers, this approach contributes to the early detection and management of ocular conditions, ultimately improving patient outcomes and reducing the global burden of vision-related ailments.