Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image

The physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms

ARIAS: Automated Retinal Image Analysis System

In this paper, a system for automated analysis of retinal images is proposed. This system segments blood vessels in retinal images and recognizes the main features of the fundus on digital color images. The recognized features were defined as blood vessels, optic disc, and fovea. An algorithm called 2D matched filters response has been proposed for the detection of blood vessels. Also, automatic recognition and localization methods for optic disc and fovea have been introduced and discussed. Moreover, a method for detecting left and right retinal fundus images has been presented

Evaluation of Publicly Available Blood Vessel Segmentation Methods for Retinal Images

Retinal blood vessel structure is an important indicator of disorders related to diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and two unsupervised retinal blood vessel segmentation methods are quantitatively compared by using five publicly available databases with the ground truth for the vessels. The parameters of each method were optimized for each database with the motivation to achieve good segmentation performance for the comparison and study the importance of proper selection of parameter values. The results show that parameter optimization does not significantly improve the segmentation performance of the methods when the original data is used. However, the methods’ performance for new data differs significantly. Based on the comparison, Soares method as a supervised approach provided the highest overall accuracy and, thus, the best generalisability. Bankhead and Nguyen methods’ performance were close to each other: Bankhead performed better with ARIADB and STARE, whereas Nguyen was better with DRIVE. Sofka method is available only as an executable and its performance matched the others only with ARIADB

Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

Two applications of shape-based morphology: blood vessels segmentation and a generalization of constrained connectivity

International audienceConnected filtering is a popular strategy that relies on tree-based image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes built from the image. Such a processing, that we called shape-based morphology, is a generalization of the existing tree-based connected operators. In this paper, two different applications are studied: in the first one, we apply our framework to blood vessels segmentation in retinal images. In the second one, we propose an extension of constrained connectivity. In both cases, quantitative evaluations demonstrate that shape-based filtering, a mere filtering step that we compare to more evolved processings, achieves state-of-the-art results

Analysis of thick and thin vessel pixel clustering for retinal blood vessel image segmentation

In this work, we revealed that digital image processing is an actual topic at present and it is widely used in various fields of medicine, including diagnosis of the eye fundus. An analysis of the dependence of the blood vessel segmentation results on the image of the eye fundus from various partitions to pixel classes corresponding to thick and thin vessels obtained by k-means clustering was mad

Multiscale approach of retinal blood vessels segmentation based on vessels segmentation with different scales

In this work, the authors developed retinal blood vessels segmentation approach using contrast limited adaptive histogram equalization, morphological filtering, k-means clustering, matched filtering for thin and thick vessels selection. The authors also applied matched filtering for thin vessels selection using the kernels which were built in order to determine the existence of line segments with different length and orientatio