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The choroid is vascular tissue located underneath the retina and suppliesoxygen to the outer retina; any damage to this tissue can be a precursor toretinal disease. Choroid is almost invisible in Enhanced Depth Imaging OpticalCoherence Tomography (EDI-OCT) images and it is hard for an ophthalmologist todetect this layer manually. This paper presents an automatic method ofchoroidal segmentation from EDI-OCT images in neutrosophic space. Inneutrosophic approach for image processing, extracting any information from animage and applying any process on it, is modeled by three sets including true,false, and indeterminacy sets. At first, we transform each image to theneutrosophic space; then, we calculate weights between each two nodes and applythe Dijkstra algorithm in order to detect Retinal Pigment Epithelium (RPE)layer. Based on RPE localization and applying gamma correction and homomorphicfilter to false set, we segment choroid layer by similar approach used for RPE.The proposed algorithm is tested on 32 EDI OCT images of Heidelberg 3D OCTSpectralis from 11 people and is compared with manual segmentation. The resultsshowed an unsigned error of 3.34 pixels (12.9 Micrometer) for macular imagesand 6.55 pixels (25.3 Micrometer) for prepapillary images; which macular erroris lower than two other methods (7.71 pixels and 9.79 pixels). Furthermore, theproposed method on prepapillary data is novel and published works areconcentrated on macular data. Identification of the boundary can help todetermine the loss or change of choroid, which can be used as features forautomatic determination of the retinal diseases.

Automatic Segmentation of Choroid Layer in EDI OCT Images Using Graph Theory in Neutrosophic Space