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.
This paper presents a non-iterative fuzzy neural classifier to improve the efficiency and training speed of the automatic face recognition system. Main issue of face recognition is that its performance deteriorates if there are variations like illumination, pose and expressions. In the proposed classifier, principal component analysis (PCA) is used for feature extraction that projects the face image into eigen space that best describes the data with reduce size of the database. These features are fed to non-iterative fuzzy neural classifier in which hidden neurons are randomly generated and output weights are calculated analytically in non-iterative manner. Fuzzy neural classifier offers fuzzy activation function which provides various normalization in face images. Experimental results on Yale Face database demonstrate that the proposed classifier performs well compared to the state-of-art recognition techniques in terms of recognition error rate and learning speed.