Automatic segmentation of macula in retinal flourescein angiography images

Retinal diseases are recurrent these days and there are many ways to scan the fundus of the eye in order to diagnose the disease. The most common type of retinal disease are Diabetic Maculopathy and Pediatric Malarial Retinopathy. Some previous studies showed that the leakage detected in retinal angiography can point to the existence of the disease. Hence, in this report, a saliency-based technique is proposed in order to detect the leakage regions in a fluorescein angiography. First, the image of a fundus is divided into intensity-based clusters of pixels using super pixels and then a saliency map is estimated. For each level of the super pixel, a compactness and intensity-based saliency cues are computed. An averaging operator is used to combine all the saliency maps obtained for each cue. Later, the final saliency map of each cue is combined using a multiplication operator to give an output of a single saliency map image. The leakage regions are finally segmented using thresholding and graph-cut segmentation techniques. It can be seen from the final result that compared to the previous techniques in leakage detection, the method proposed in this paper yields better results. The principle target of this paper is to make and approve programming to consequently segment leakage area in true clinical Fundus fluorescein angiography (FFA) images of subjects with diabetic macular edema ( DME). Our segmentation strategy can reproducibly and precisely evaluate the area of leakage of clinical evaluation FA images and is compatible with master manual segmentation. Of an estimated 285 million individuals worldwide with diabetes mellitus, approximately one third have signs of Diabetic Retinopathy (DR) and of these, a further one third of DR is vision-threatening DR, including DME.