  The choroid is vascular tissue located underneath the retina and supplies
oxygen to the outer retina; any damage to this tissue can be a precursor to
retinal disease. Choroid is almost invisible in Enhanced Depth Imaging Optical
Coherence Tomography (EDI-OCT) images and it is hard for an ophthalmologist to
detect this layer manually. This paper presents an automatic method of
choroidal segmentation from EDI-OCT images in neutrosophic space. In
neutrosophic approach for image processing, extracting any information from an
image and applying any process on it, is modeled by three sets including true,
false, and indeterminacy sets. At first, we transform each image to the
neutrosophic space; then, we calculate weights between each two nodes and apply
the Dijkstra algorithm in order to detect Retinal Pigment Epithelium (RPE)
layer. Based on RPE localization and applying gamma correction and homomorphic
filter 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 OCT
Spectralis from 11 people and is compared with manual segmentation. The results
showed an unsigned error of 3.34 pixels (12.9 Micrometer) for macular images
and 6.55 pixels (25.3 Micrometer) for prepapillary images; which macular error
is lower than two other methods (7.71 pixels and 9.79 pixels). Furthermore, the
proposed method on prepapillary data is novel and published works are
concentrated on macular data. Identification of the boundary can help to
determine the loss or change of choroid, which can be used as features for
automatic determination of the retinal diseases.
