Retinal blood vessels extraction using probabilistic modelling

© 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.The Department of Information Systems, Computing and Mathematics, Brunel University

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

Implementation of Pre-processing and Efficient Blood Vessel Segmentation in Retinopathy Fundus Image

The human retina is a light receptive tissue and its enormously rich in blood vessels for its high physiological stress and dysfunction of the retinal vasculature can effect from several diseases. Diabetic retinopathy is caused due to complications of diabetes, which can eventually develop new blood vessels at the back of the retina and it can lead to blur vision or loss of vision. This work describes the problems of retinopathy associated with diabetic patients and premature babies. We propose methods for the preprocessing and efficient segmentation method to support measurement of the openness of the MTA, including image enhancement techniques like morphological operations, efficient luminance component construction and bank of Gabor filters to segment retinal blood vessels. Finally an image cropping is used to separate inferior and superior part of this segmented image for the effective and detailed analysis of the vascular structure in the fundus eye images. Certain retinal disorders, if not detected in time, can cause serious problems like blur vision and blindness in patients. The implementation and the performance of the various edge detection methods like Canny, Sobel and Gabor filters are based on visual perception. It has been concluded that in case of natural images such as retinal fundus image a Gabor filter yielded better results in segmentation of blood vessels as compared to edge detection methods of Canny and Sobel. DOI: 10.17762/ijritcc2321-8169.15066

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

Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them

Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe

Measurement of retinal vessel widths from fundus images based on 2-D modeling

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel

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