The Study On Retinal Vascular Segmentation In Fundus Images

Some common diseases in daily life,such as hypertension,nephritis,diabetes and cardiovascular disease will cause retinal vascular disease,so observing the changes of vascular morphology can be preliminary estimate or diagnosis of some diseases of the human body.As a result,it is necessary to use digital image processing technology to detect and analyze the retinal vessels,focusing on whether there is any abnormal shape,in order to help doctors diagnose the disease.The use of digital image processing technology has the advantages of fast processing speed,objective analysis,comparability and so on,avoiding the subjectivity and uncertainty of artificial analysis.Therefore,it is of great clinical significance to study retinal vessel segmentation algorithm in fundus images.In this paper,the methods of retinal blood vessels in fundus images are studied.The main work is as follows:(1)A retinal vascular enhancement algorithm based on wavelet transform and retinex is proposed.Firstly,the image is decomposed by wavelet to obtain low-frequency and high-frequency coefficients.Secondly,the low-frequency image is processed by multi-scale retinex algorithm and the high-frequency image is enhanced by wavelet layering.Then these two images are reconstructed by wavelet.Finally,the clarity,contrast and detail salience of the retinal vascular image are greatly improved,and good results are obtained.(2)An improved multiscale single-channel linear tracking segmentation algorithm is proposed.The basic idea is to start tracking from a part of the selected seed pixels in the image,and to give high confidence to the pixels that meet the conditions in the tracking process.After tracking,the confidence matrix of all the pixels is quantized to obtain the vascular network.Then the noise is processed by combining connected domain labeling and median filtering,which can not only remove speckle noise,but also eliminate the edge of retinal image as continuous block target noise.The morphological theory is applied to the segmentation process,which extends the breadth and depth of the algorithm to some extent.(3)A segmentation algorithm based on conditional random field model is proposed.The algorithm consists of two parts: the first part makes initial judgment on the label of a node in the graph according to the local characteristics and the second part is to estimate the weight parameters according to the relationship between adjacent nodes.The univariate potential function and bivariate potential function are calculated by using two-dimensional Gabor wavelet features,Zana and Klein proposed vascular enhancement techniques respectively.Then,the classifier is trained by selecting a certain sample,and the output of the classifier is used as the input of conditional random field model.The degree of influence of the classification result on the final result is determined by adjusting the weight parameters.The extended conditional random field model is used to segment retinal blood vessel image.The segmentation results show that the accuracy of blood vessel location is high,and the phenomenon of blood vessel adhesion can be effectively avoided.The blood vessel is smoother and the boundary location is more accurate.At the same time,the fine blood vessels can be segmented.