Abstract
Artificial Intelligence (AI) based Machine Learning (ML) is gaining more attention from researchers. In ophthalmology, ML has been applied to fundus photographs, achieving robust classification performance in the detection of diseases such as diabetic retinopathy, retinopathy of prematurity, etc. The detection and extraction of blood vessels in the retina is an essential part of various diagnosing problems associated with eyes, such as diabetic retinopathy. This paper proposes a novel machine learning approach to segment the retinal blood vessels from eye fundus images using a combination of color features, texture features, and Back Propagation Neural Networks (BPNN). The proposed method comprises of two steps, namely the color texture feature extraction and training the BPNN to get the segmented retinal nerves. Magenta color and correlation-texture features are given as input to the BPNN. The system was trained and tested in retinal fundus images taken from two distinct databases. The average sensitivity, specificity, and accuracy obtained for the segmentation of retinal blood vessels were 0.470%, 0.914%, and 0.903% respectively. Results obtained reveal that the proposed methodology is excellent in automated segmentation retinal nerves. The proposed segmentation methodology was able to obtain comparable accuracy with other methods.
Highlights
Utilizing computer-assisted diagnosis of retinal fundus images is becoming an alternative to the manual inspection known as direct ophthalmoscopy
A series of feature vectors is generated from the data being processed and a classifier is trained by using the labels assigned to the data
This paper proposed a novel method for segmenting nerves of retinal images using the combination of color and texture features with Back Propagation Neural Networks (BPNN)
Summary
Utilizing computer-assisted diagnosis of retinal fundus images is becoming an alternative to the manual inspection known as direct ophthalmoscopy. The majority of the proposed segmentation methods focus on optimizing the preprocessing and vessel segmentation parameters separately for each dataset These approaches can often achieve high accuracy for the optimized dataset, whereas their application to other datasets has reduced accuracy. Nowadays many segmentation methods rely on machine learning [4] concepts combined with traditional segmentation techniques for enhancing the segmentation accuracy, by providing data statistical analysis to support segmentation algorithms These machine-learning concepts can be broadly categorized into unsupervised and supervised approaches, based on the use of labeled training data. Unsupervised approaches use predefined feature vectors without any class labels, where similar samples are gathered in distinct classes This clustering is based on some assumptions about the structure of the input data, i.e. two classes of input data where the feature vectors of each class are similar to each other (vessel and not vessel). This similarity metric can be complex or defined by a simple metric such as pixel intensities
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