Feature Extraction Through Segmentation of Retinal Layers in SDOCT Images for the Assessment of Diabetic Retinopathy

摘要

Diabetic mellitus causes microvasculature changes in the retina which leads to diabetic retinopathy and may cause blindness if left unchecked. Spectral-Domain Optical Coherence Tomography (SDOCT) is a noninvasive imaging modality which could give precise information about the retinal layers. SDOCT retinal images of 75 subjects with uncontrolled diabetic mellitus for more than 2 years duration and images of 30 subjects with controlled diabetes or in normal condition are considered. The speckle noise in the images is smoothened using anisotropic diffusion filtering technique, and segmentation of Retinal Nerve Fiber layer (RNFL) along with Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL) complex is performed using the axial gradient canny edge detection combined with a level set method. Textural features are obtained from the segmented layers, and classification of abnormality is done using SVM. The results showed that the retinal nerve fiber layer along with GCL+IPL complex thickness was reduced in subjects with even minimal diabetic retinopathy.