An active contour model for segmenting and measuring retinal vessels

This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a "Ribbon of Twins" active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with sub-pixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement

Automated measurements of retinal bifurcations

This paper presents an analysis of the bifurcations of retinal vessels. The angles and relative diameters of blood vessels in 230 bifurcations were measured using a new automated procedure, and used to calculate the values of several features with known theoretical properties. The measurements are compared with predictions from theoretical models, and with manual measurements. The automated measurements agree with the theoretical prediction measurements with slightly different bias. The automated method can measure a large number of retinal bifurcations very rapidly, and may be useful in correlating bifurcation geometry with clinical conditions

A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i4.625

Two Novel Retinal Blood Vessel Segmentation Algorithms

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose two automatic blood vessel segmentation methods. The first proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the contents of several binary images resulting from vessel width dependent modified morphological filters on normalized retinal images. In the second proposed algorithm the blood vessel is segmented using normalized modified morphological operations and neuro fuzzy classifier. Normalized morphological operations are used to enhance the vessels and neuro fuzzy classifier is used to segment retinal blood vessels. These methods are applied on the publicly available DRIVE database and the experimental results obtained by using green channel images have been presented and their results are compared with recently published methods. The results demonstrate that our algorithms are very effective methods to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i3.582

Retinal vascular geometry: novel biomarkers of progression from diabetes to diabetic retinopathy

Diabetic retinopathy (DR) remains a major cause of blindness in the developed countries. Geometric and Haemodynamic features are still not widely investigated, especially as biomarkers of progression to DR. Most studies rely on disease vs control design, which introduces errors and limitations, given the diversity of the retinal vascular geometry (small and large vessels). Our studies have mainly focused on investigating the vascular changes within the same patients during a four year period that includes the last three years of pre-DR and 1st year of DR (onset)

Automatic Segmentation of Retinal Vasculature

Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper, we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering. This image is then converted to a gray scale using weighted scaling. The vessel edges are enhanced by boosting the detail curvelet coefficients. Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification. Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels. The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods. The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms.Comment: Published at IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 201

An efficient technique for retinal vessel segmentation and denoising using modified isodata and CLAHE

Retinal damage caused due to complications of diabetes is known as a Diabetic Retinopathy (DR). In this case, the vision is obscured due to damage of tiny retinal blood vessels. These tiny blood vessels may cause leakage that affect the vision and can lead to complete blindness. Identification of these new retinal vessels and their structure is an essential for analysis of DR. Automatic blood vessel segmentation plays a significant role to assist subsequent automatic methodologies that aid to such analysis. In literature, most authors have used computationally-hungry strong preprocessing steps followed by a simple thresholding and postprocessing steps. This paper proposed an arrangement of simple preprocessing steps that consist of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and a difference image of green channel from its Gaussian blur filtered image to remove local noise or geometrical objects. The proposed Modified Iterative Self Organizing Data Analysis Technique (MISODATA) has been used for segmentation of vessel and non-vessel pixels based on global and local thresholding. Finally, postprocessing steps have been applied using region properties (area, eccentricity) to eliminate the unwanted regions/segments, nonvessel pixels, and noise. A novel postprocessing steps are used to reject misclassified foreground pixels. The strategy has been tested on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases. The average accuracy rates of 0.952 and 0.957 with average sensitivity rates 0.780 and 0.745 along with average specificity rates of 0.972 and 0.974 were obtained on DRIVE and STARE datasets, respectively. The performance of the proposed technique has been assessed comprehensively. The acquired accuracy, robustness, low complexity, and high efficiency make the method an efficient tool for an automatic retinal image analysis. The proposed technique perform well as compared to the existing strategies on the online available databases in term of accuracy, sensitivity, specificity, false positive rate, true positive rate, and area under receiver operating characteristic (ROC) curve

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