Anatomical Structure Detection Of Retinal Image And Lesion Analysis

With the continuous improvement of people's quality of life,the incidence of diabetes show increasing trend year by year.Diabetes retinopathy(DR)is one of the major complications associate with diabetic microvascular disease and a main reason causes blindness and visual impairment.The pathological information of DR can be analyzed from the retinal image.Due to the diverse pathologic structures of different disease stages and the uniqueness of human retinal micro-vascular system,we will confront an extremely tough challenge when taking the research.In this paper,retinal images are used as objects of study and the major work focuses on the analysis of anatomical structure and lesion regions.The main research contents are listed as follows:1.Retinal image preprocessing.This task contains retinal image background homogenization,noise pixel attenuation and region of interest extraction.And the result of preprocessing has an effect on the accuracy of anatomic structure detection and lesion analysis.2.Retinal anatomical structure detection.It includes vascular segmentation,optic disc detection and macular localization.We propose a method based on Weber transformation and multi-scale spatial analysis to segment vessels.Then enhance the vessel pixels through two key features,edge strength and ridge strength.Optic disc(OD)acts as the diagnostic basis of glaucomatous optic nerve damage,we can use its essential characteristics combined with Sobel operator,Canny operator and Hough transformation to get the final detected OD.And as for macular localization,an approach of template matching is used for getting the precise location.3.Retinal lesion regions detection.The main detecting target consists of micro-aneurysms,hemorrhages and exudates.In the detection of micro-aneurysms,a multi-scale Gaussian kernel function is used for similarity matching,and then employing Support Vector Machine(SVM)to classify the collected candidates.Compared with other methods,the proposed method has a higher accuracy.As for hemorrhages,the candidate regions are obtained by the mathematical morphology with multi-scale linear structure.And then process the regions into super-pixel blocks by using SLIC and calculate its classification features,which are used for classifying hemorrhages by means of grayscale voting algorithm.The last part lies in exudates detection,we first use dynamic window and morphological reconstruction to get the candidate regions.Then the features are calculated corresponding to the super-pixel blocks,and design an Artificial Neural Networks(ANN)classifier.