Detection of DME by Classification and Segmentation Using OCT Images

Optical Coherence Tomography (OCT) is a developing medical scanning technique proposing non- protruding scanning with high resolution for biological tissues. It is extensively employed in optics to accomplish investigative scanning of the eye, especially the retinal layers. Various medical research works are conducted to evaluate the usage of Optical Coherence Tomography to detect diseases like DME. The current study provides an innovative, completely automated algorithm for disease detection such as DME through OCT scanning. We performed the classification and segmentation for the detection of DME. The algorithm used employed HOG descriptors as feature vectors for SVM based classifier. Cross-validation was performed on the SD-OCT data sets comprised of volumetric images obtained from 20 people. Out of 10 were normal, while 10 were patients of diabetic macular edema (DME). Our classifier effectively detected 100% of cases of DME while about 70% cases of healthy individuals. The development of such a notable technique is extremely important for detecting retinal diseases such as DME.