Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

Abstract

Optical Coherence Tomography (OCT) constitutes an imaging technique that is increasing its popularity in the ophthalmology field, since it offers a more complete set of information about the main retinal structures. Hence, it offers detailed information about the eye fundus morphology, allowing the identification of many intraretinal pathological signs. For that reason, over the recent years, Computer-Aided Diagnosis (CAD) systems have spread to work with this image modality and analyze its information. A crucial step for the analysis of the retinal tissues implies the identification and delimitation of the different retinal layers. In this context, we present in this work a fully automatic method for the identification of the main retinal layers that delimits the retinal region. Thus, an active contour-based model was completely adapted and optimized to segment these main retinal boundaries. This fully automatic method uses the information of the horizontal placement of these retinal layers and their relative location over the analyzed images to restrict the search space, considering the presence of shadows that are normally generated by pathological or non-pathological artifacts. The validation process was done using the groundtruth of an expert ophthalmologist analyzing healthy as well as unhealthy

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

“…Novosel [101] developed an algorithm to segment layers and lesions. González-López [105] created an algorithm tolerant to noisy scenarios. Gopinath [108] developed a method that not requires any preprocessing step.…”

“…Comparisons between the method of Novosel [101] and others was difficult, because there was no pattern of images and reference standard. González-López [105] sugested a model in which a completely fair comparison with the states-of-the-art seems extremely complicated because they used more than one dataset, each one with their own setting. Duan [110] created a method in which there is not quantitative analysis of the results and nothing about the database was mencioned.…”

“…The review indicates many types of preprocessing techniques: dilation and erosion [5], median filter [27,107,111], gaussian filter [11,100,101], wiener filter [11,81,99], binary image [26,100], gradient image [26,114], anisotropic difusion filter [5,97,99], image aligment [19,98,103], attenuation coefficient [101], enhanced contrast [1,105], image flattenig [106,114], resize the image [1,107], edge flow [112], sparse filter [112], normalization [81], green channel [81], greyscale [1], morphological operations [1] and others. In Figure 2 is shown the improvements of preprocessing applied to OCT image.…”

“…Some segmentation techniques applied to OCT images are: snakes or active contour, it is a method used to circumvent objects in an image [105]; graph search, this technique starts from a starting point to an ending point, repeating the process, trying to find the best path [5,26,27]; Dijkstra shortest path search, it is an algorithm that seeks to find the shortest path between two nodes [26,99,107]; local gaussian distribution fitting (LGDF), local entropy defines gray level weights [11]; curvature-based surface cutting, it can be flattened into the plane with low metric distortion [11]; ReLayNet, it is an architecture used to segment retinal layers [80,117]; speed-up robust features (SURF), it is a method used in tasks like object recognition, classification etc [115]; adjusted mean arc length (AMAL), it enables to pass the load limit and turning points, and consequently to follow the post-critical equilibrium trajectories [26]; gradient vector flow (GVF), it is an algorithm that locates object edges [96,118,119].…”

“…González-López [105] developed an automatic segmentation method for retinal boundary layers. The proposed technique was based on Snakes or Active Contours and it used 40 OCT images.…”