Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based Features

Abstract

This paper focuses on the issue of extracting retina vessels with supervised approach. Since the green channel in the retina image has the best contrast between vessel and non-vessel, this channel is used to separate vessels. In our approach we are proposing a technique of using gray-level co-occurrence matrix method for composition of the retinal images. It is based on fact that the co-occurrence matrix of retina image describes the transition of intensities between neighbour pixels, indicating spatial structural information of retina image. So, we first extract the features vector based on specified characteristics of the gray-level co-occurrence matrix and then we use these features vector to train a neural network approach for the classification method which makes our proposed approach more effective. Obtained results from the experiments in DRIVE and STARE database shows the advantage of the proposed method in contrast to current methods. This advantage is evaluated by the criteria of sensitivity, specificity, area under ROC and accuracy. The result of such a conversion as the input vector of a multilayer perceptron neural network will be trained and tested. Although in recent years different methods have been presented in this respect, but results of simulation shows that the proposed algorithm has a very high efficiency than the other researches.

Retinal vessel segmentation using neural network

“…Rahebi and Hardalaç [7] proposed an NN-based retinal vessel segmentation using GLCM features. This method applies a median filter and local mapping method to enhance the input image in preprocessing stage.…”

Automated techniques for blood vessels segmentation through fundus retinal images: A review

“…Rahebi and Hardalaç () proposed a method which performed retinal vessels segmentation through derived features from gray level co‐occurrence matrix of image. Firstly, the finest band of colored retinal image was selected for pre‐processing and then modifying the brightness in retinal image by applying new local processing function.…”

A computational modeling for the detection of diabetic retinopathy severity

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