An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

Purpose Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

Fractional and B-COSFIRE filter based approach for efficient segmentation of retinal blood vessels

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

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.v4i3.5829

Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation

Over the last decades, various methods have been employed in medical images analysis. Some state-of-the-arts techniques such as deep learning have been recently applied to medical images analysis. This research proposes the application of deep learning technique in performing segmentation of retinal blood vessels. Analyzing and segmentation of retina vessels has assisted in diagnosis and monitoring of some diseases. Diseases such as age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis and choroidal neovascularization can be effectively managed by the analysis of retinal vessels images. In this work, a Deep Convolutional Encoder-Decoder Architecture for the segmentation of retinal vessels images is proposed. The proposed method is a deep learning system composed of an encoder and decoder mechanism allows a low resolution image set of retinal vessels to be analyzed by set of convolutional layers in the encoder unit before been sent into a decoder unit for final segmented output. The proposed system was evaluated using some evaluation metrics such as dice coefficient, jaccard index and mean of intersection. The review of the existing works was also carried out. It could be shown that the proposed system outperforms many existing methods in the segmentation of retinal vessels images.

Diabetic retinopathy (DR) is the major ophthalmic disorder because of variation in veins structure which may cause blindness. The retinal vein morphology distinguishes the progressive phases of various sight debilitating maladies and consequently clears an approach to characterize its seriousness. The proposed method for retinal blood vessels detection consists of two major processes: denoising and vasculature segmentation. First, we used denoising preprocessing steps which comprises of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement along with morphological filters to remove low frequency noise, followed by masking to excerpt Region Of Interest (ROI) and difference image of low pass filter to suppress high frequency noise. Adaptive thresholding has been used for vessels segmentation, followed by postprocessing to eliminate unconnected pixels and to obtain Vessel Location Map (VLM). Dilate operation has been used to enhance vessels diameter. In the second step, Combination Of Shifted Filter Responses (B-COSFIRE) for vasculature enhancement along with adaptive thresholding has been applied to segment vessel and background pixels. B represents the bar/vessel like structure. Finally, using pixel by pixel AND operation between VLM and the output of adaptive thresholding, to obtain desired binary image. The proposed framework has been validated on DRIVE and STARE images datasets and compared with other recent approaches for retinal blood vessels segmentation. The proposed scheme provides good results in the term of Accuracy (Acc), Sensitivity (Se) and Specificity (Sp) as compared to other competing methods.

Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.

Vessel segmentation is very important in an automatic screening system for fundus images. Vessels are often segmented and removed from retinal images before the other residual lesions are detected. Incomplete vessel removal usually causes a false positive in lesion detection, especially for Microaneurysms detection. Segmenting vessels in spatial image domain makes miss detection due to non illumination and noises in retinal images. Non-illumination problem can be disregarded by segmenting the vessels in spatial-frequency domain, which invariant subbands can be ignored. This paper presents a new retinal blood vessel segmentation method based on fractal dimension in spatial-frequency domain of retinal images. The fractal dimension value of each pixel is computed in order to extract the vessels from their retinal background. The performance of the proposed method is evaluated and compared with the experts' diagnosis in the STARE database.

An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering

Artery vein classification in fundus images using serially connected U-Nets

In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with PCA and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The average accuracy rate obtained for the gray level image with PCA after the study was 0,9443, while the average accuracy rate obtained for the green channel was 0,9685. The study was performed using 40 images in the DRIVE data set which is one of the most common retina data sets known.

The retinal vessel segmentation task plays an important role in clinical diagnosis and treatment, especially in cardiovascular diseases such as diabetic retinopathy and hypertensive retinopathy. Recently, the fully convolutional neural network, as a popular learning-based segmentation method, has been demonstrated to yield highly segmentation performance in vessel wall segmentation tasks. However, the major network factors affecting the performance of segmentation are still not obvious. This paper uses the single-factor control variable method to investigates the effects of network architectures (FCN network and U-Net network) and other network factors (pooling times, patch size, number of skip connection, and network depth) on retinal blood vessel segmentation. Our experiments are performed on two public fundus image database DRIVE and STARE. The results show that U-net is better than FCN and skip connections, proper pooling times, dilated convolution is vital to obtain better performance of retinal vessel segmentation.

In this research, retinal blood vessel fundus images will be segmented. Moreover, bifurcation point of the segmented blood vessel images will be determined. The data used for this research was obtained from fundus image database of DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The preprocessing phases contain of several steps such as green channel extraction, histogram equalization and optic disc elimination. Meanwhile, the segmentation phase was performed by combining two filtering methods. These methods are median and derivative of Gaussian filter. Median filter was used to reduce the noise on retinal fundus image such as hard and soft exudates as well as dot and blot hemorrhages. Combination of these two filtering methods were also employed to determine the bifurcation point of segmented blood vessel image. The bifurcation points and the result from blood vessel segmentation were among two of the unique parameters of retinal fundus image. These parameters will be used eventually as unique features of the advanced research which is a biometric system to identify an individual based on his/her unique retinal pattern.

Automated retinal blood vessel segmentation is a fundamental component in computer aided retinal disease screening system and diagnosis. This paper presents a novel method of Hausdorff symmetry operator for automatic centerline pixel selection towards retinal blood vessel segmentation. Centerline pixels are determined by considering geometrical symmetry (distance and orientation) and Hausdorff distance based point set matching at the centerline pixel. This is performed in subpixel resolution to achieve higher accuracy. Then K-means clustering is applied to remove false centerline pixels. The selected centerline pixels act as seed points to be used in region growing to segment the retinal blood vessels. Our proposed method is evaluated on DRIVE and STARE databases. The experimental results demonstrate that the performance of the proposed method is comparable with state-of-the-art techniques. The advantages of the proposed method include its ability to correctly segment thin blood vessels, vessels containing light reflex, and disc area is not misclassified as vessels.