Vessel segmentation in images of optical coherence tomography using shadow information and thickening of Retinal Nerve Fiber Layer

The correct segmentation of blood vessels in optical coherence tomography (OCT) images is an important requirement for better diagnosis of many retinal diseases. Although OCT blood vessel segmentation is often performed by applying vessel detection methods on 2D projection of OCT datasets, some papers investigate the vessel segmentation on OCT slices. The presence of shadows in outer retinal layers is established as the main factor for vessel localization; however, the shadow information fails to localize many important blood vessels. The proposed method is based on anatomical changes of Retinal Nerve Fiber Layer (RNFL) in presence of vessels. In this paper we find the thickening of RNFL by applying a layer segmentation algorithm on OCT slices and combine this information with shadow localization. Furthermore, a vessel detection method based on curvelet transform is also applied on 2D projection of OCTs to be added to localized vessels from OCTs. The results show that combination of vessel detection on 2D projection with vessel localization on OCTs can improve the accuracy up to 0.96 which is promisingly higher than older methods.