GATAC: Gradient and adaptive threshold integrated active contour for Diabetic Macular Edema segmentation

Diabetic macular edema (DME) caused the major issue of visual disorder, and hence the prediction of DME at premature stage through Optical Coherence Tomography (OCT) is broadly utilized to diagnose the retinal disorders. The term diabetic maculopathy is caused due to the pathological modification of pre-retinal and intra-retinal region in macula. Nevertheless, the intra-retinal modification composed of two various interconnected elements, like retinal ischemia and retinal edema. Hence, the ultimate goal of this research is to design and develop a Gradient-based Adaptive thresholding with Active contour model for the detection of DME. Initially, the input image is given to the layer segmentation module in which the layer segmentation is carried out based on Gradient-based Adaptive thresholding integrated Active contour model. Moreover, the implementation of the proposed method is carried out in the PYTHON tool using Optical Coherence Tomography Image Retinal Dataset. In addition, the performance of the proposed method is analyzed using three performance metrics, namely segmentation accuracy, dice coefficient, and Jaccard coefficient. The performance of the developed model is implemented, and then compared with the existing methods in order to reveal the effectiveness of the developed method. Thus, the developed GATAC segmentation method achieved enhanced results with high accuracy of 92.35%, Jaccard coefficient of 0.9235and dice coefficient of 0.5292.