Retinal blood vessel segmentation based on fractal dimension in spatial-frequency domain

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.

High blood pressure is a condition that produces various negative effects on the human eye, including choroidopathy, retinopathy, and optic neuropathy. The pathology of the human retina, which results from high blood pressure, is hypertensive retinopathy. Hypertension causes narrowing of retinal veins and arteries, leading to retinal cotton wool spots, hemorrhages, arteriovenous nipping, and even papilledema. One of the syndromes without any visible signs is hypertensive retinopathy which can lead to serious vision loss or even death (due to severe papilledema). The scores or types of high blood pressure retinopathy vary based on their seriousness—several eye examination methods for the detection of high blood pressure retinopathy. Fundus fluorescein angiography, ophthalmoscopy, and fundus photography are some widely used techniques. Various researchers are designing completely integrated decision support systems the detect high blood pressure retinopathy. The accurate segmentation of retinal blood vessels affects the consistency of retinal image analysis used in modern ophthalmologic diagnostics. One of the most important steps in any retinal blood vessel segmentation method is image enhancement. The accuracy of the contrast across the picture determines the segmentation's reliability. We offer an image-enhancing technique that addresses these challenges regarding the gravitational force (GF) and lateral inhibitory network (LIN). Our method is well adapted for vessel detection, according to the findings.KeywordsHypertensive retinopathyContrast enhancementVessel detection

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

The segmented blood vessel in retinal images is an important indicator in medical treatment. In 2007, Ricci and Perfetti proposed a simple and efficient blood vessel segmentation method based on line operator. However, this scheme makes some false segmentation when it is used to process the pixels which are close to a thick blood vessel. To overcome the above problem, a novel retinal blood vessel segmentation method based on line operator and edge detector, also called (RBVSLE), was proposed. The RBVSLE enhances the contrast of the green-channel of the retinal images. Next, the RBVSLE creates the edge map, which is denoted as vessel growth seed map, by using a canny detector. Finally, the RBVSLE segments vessel pixel by line operator from vessel growth seed map. Experimental results confirm the accuracy rate of RBVSLE is approximately 94% which is significantly better than other methods.

Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel's center, the central vessel reflex. A new method for automatic segmentation was developed and tested. A supervised method for retinal vessel segmentation in the images of multi-ethnic school children was developed based on ensemble classifier of bootstrapped decision trees. A collection of dual Gaussian, second derivative of Gaussian and Gabor filters, along with the generalized multiscale line strength measure and morphological transformation is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with the central reflex. The methodology is evaluated on CHASE_DB1, a relatively new public retinal image database of multi-ethnic school children, which is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset. The segmented retinal images from the CHASE_DB1 database produced best case accuracy, sensitivity and specificity of 0.96, 0.74 and 0.98, respectively, and worst case measures of 0.94, 0.67 and 0.98, respectively. A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.

<span>Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.</span>

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

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.

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.

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.

Segmentation of retinal blood vessels is important for diagnosis of many retinal diseases. Precise segmentation of complete vessel-map is still a challenge in low contrast regions of fundus images. Vessel pixels belonging to these regions, such as, fine vessel-endings and boundaries of vessels, get merged in the neighboring vessel-like texture. This paper proposes a novel retinal vessel segmentation algorithm which handles the background vessel-like texture in a sophisticated manner without harming the vessel pixels. In this work, first we enhance all possible vessel-like features of fundus at different `levels' using 2-D Gabor wavelet and Gaussian matched filtering. At each `level', texture is suppressed using Local Laplacian filter while preserving the vessel edges. The resulting images are combined to produce a maximum response image with enhanced vessels of different thicknesses and suppressed texture. This handcrafted image is used to train the deep U-net model for further suppression of non-vessel pixels. Proposed segmentation method is tested on publicly available DRIVE and STARE databases. The algorithm has produced state-of-the-art results. It has performed outstandingly well in terms of sensitivity measure which is most affected with the correct segmentation of fine vessels and vessel-boundary pixels present in low-contrast regions.

The structure of blood vessels play a crucial role in diagnoses of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The correct segmentation of retinal blood vessels is a crucial step in the study of retinal fundus images. We proposed a simple unsupervised approach by using a combination of Hessian based approach and intensity transformation approach. We have applied CLAHE for enhancing the contrast of the retinal fundus images. An enhanced version of PSO algorithm is applied for contextual region tuning of CLAHE. Morphological filter and Wiener filter are used to de-noise the enhanced image. The eigenvalues are obtained from the Hessian matrix at two different scales to extract thick and thin vessel enhanced images separately. The intensity transformation approach is separately applied to the enhanced image to maximize the vessel details. Global Otsu thresholding is applied on intensity transformed image and thick vessel enhanced image whereas ISODATA local thresholding is applied on thin vessel enhanced image. Finally, a simple post-processing step based on the region parameters such as area, eccentricity, and solidity is used. The region parameters are obtained for each connected component in input binary images. The threshold values of region parameters are empirically investigated and applied to each of the three binary images to remove the non-vessel components. The thresholded images are combined by applying logical OR operator, which resulted in the final segmented binary image. We assessed our developed framework on the open-access CHASE_DB1 and DRIVE datasets, achieving a sensitivity of 0.7776 and 0.7851, and an accuracy of 0.9505 and 0.9559 respectively. These results outperform several state-of-the-art unsupervised methods. The reduced computational complexity and significantly improved evaluation metrics advocates for its use in the automated diagnostic systems for retinal image analysis.

Vascular diseases cause tortuosity in the retinal blood vessel's structure which can be identified using fundus images.The computerized retinal vessel tortuosity diagnosis is suffered from high time consuming and less accuracy due to the less effective retinal vessel segmentation algorithms.The success of retinal vessel tortuosity diagnosis depends on the performance quality of the vessel segmentation algorithm.Hence, this research proposes a new retinal vessel segmentation method, namely 'Retinal Vessel Segmentation using CLAHE and VJSTFD (RVSCV)'.The proposed segmentation method separates the vessel region from the background, tissue region, and Optic Disc (OD).It is constituted by two algorithms, viz.(i) vessel enhancement based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and (ii) Vessel Joining algorithm based on vessel Skeletonization and Tetra vessel Flow Directions (VJSTFD).The CLAHE method, which is an existing method, enhances the vessel structure.The main contribution of this paper is the VJSTFD algorithm, which is a novel method to segment the vessel region from the enhanced fundus image.The VJSTFD algorithm makes a binary segmentation and joints the broken vessels in a novel approach through vessel skeletonization and tetra vessel flow directions-based vessel extension.The specialty of this approach is that minute vessels are also captured for segmentation even at the low contrast level because of the proposed preeminence vessel joining approach that is powered by tetra direction-based vessel searching.The CHASE_DB, RITE_DB, and STARE_DB databases have been involved in the testing process.These databases have retinal fundus images which clearly exposes the retinal vessel areas.Experimental results reveal that the proposed RVSCV method accomplishes the overall average F-score of 0.9580, along with the average segmentation accuracy of 96.73% in retinal vessel segmentation.The proposed method can be activated as a tool for ophthalmologists to extract the vessel course essentially.

Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels 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.