Automatic Segmentation Of Blood Vessels Research Articles

Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images

Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images

IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.

Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images

With recent advanced technologies, various automated diagnosis tools were developed to prevent retinal diseases. The automatic segmentation of blood vessels can help detect various retinal diseases and also assist in reducing doctors’ workload. In existing, numerous techniques have been established to segment RBV automatically. But, they failed to provide better accuracy because of higher computational complexity and lower efficiency. Thus, the proposed work introduces an effective hybrid deep learning technique for RBV segmentation and classification of vessels. Initially, the retinal images are pre-processed to enhance the image quality by performing two steps such as image cropping and colour channel conversion. From the pre-processed images, the most important regions are segmented through a new Enhanced Fuzzy C-Means (EFCM) clustering scheme. During the segmentation process, the retinal images are clustered according to the thickness of the blood vessels, which helps minimize the computational complexity. After segmentation, a hybrid deep learning technique like DenseNet and ShuffleNet is introduced to perform feature extraction and classification. The experimental setup is done by the Python platform using the databases like DRIVE (Digital Retinal Images for Vessel Extraction), STARE (STructured Analysis of the Retina), and HRF (High Resolution fundus). Using the DRIVE dataset, the proposed model achieves an accuracy of 99%, the attained accuracy value of the STARE dataset is 98%, and the HRF dataset obtains an accuracy of 98%. The result analysis proves that the proposed hybrid deep learning technique is more efficient than the state-of-the-art techniques.

Optimized ResUNet++-Enabled Blood Vessel Segmentation for Retinal Fundus Image Based on Hybrid Meta-Heuristic Improvement

In recent years, several studies have undergone automatic blood vessel segmentation based on unsupervised and supervised algorithms to reduce user interruption. Deep learning networks have been used to get highly accurate segmentation results. However, the incorrect segmentation of pathological information and low micro-vascular segmentation is considered the challenges present in the existing methods for segmenting the retinal blood vessel. It also affects different degrees of vessel thickness, contextual feature fusion in technique, and perception of details. A deep learning-aided method has been presented to address these challenges in this paper. In the first phase, the preprocessing is performed using the retinal fundus images employed by the black ring removal, LAB conversion, CLAHE-based contrast enhancement, and grayscale image. Thus, the blood vessel segmentation is performed by a new deep learning model termed optimized ResUNet[Formula: see text]. As an improvement to this deep learning architecture, the activation function is optimized by the J-AGSO algorithm. The objective function for the optimized ResUNet[Formula: see text]-based blood vessel segmentation is to minimize the binary cross-entropy loss function. Further, the post-processing of the images is carried out by median filtering and binary thresholding. By verifying the standard benchmark datasets, the proposed model outperforms and attains enhanced performance.

Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods

Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation masks remains unexplored. In this paper, we perform a pilot study to assess the clinical viability of automatically generated segmentation masks in the diagnosis of diseases affecting retinal vascularization. Five ophthalmologists with clinical experience were asked to participate in the study. The results demonstrate low classification accuracy, inferring that generated segmentation masks cannot be used as a standalone resource in general clinical practice. The results also hint at possible clinical infeasibility in experimental design. In the follow-up experiment, we evaluate the clinical quality of masks by having ophthalmologists rank generation methods. The ranking is established with high intra-observer consistency, indicating better subjective performance for a subset of tested networks. The study also demonstrates that objective metrics are not correlated with subjective metrics in retinal segmentation tasks for the methods involved, suggesting that objective metrics commonly used in scientific papers to measure the method's performance are not plausible criteria for choosing clinically robust solutions.

Width Attention based Convolutional Neural Network for Retinal Vessel Segmentation

The analysis of the vascular tree is a fundamental part of the clinical assessment of retinal images. The diversity of blood vessel calibers and curvatures, as well as the ocular vascular alterations derived from the progress of certain diseases, make automatic blood vessel segmentation a challenging task. In this paper, a novel Width Attention-based Convolutional Neural Network, called WA-Net, is proposed. In the WA-Net, a fundus image is decomposed into multiple channels by a layer of Distorted Second-Order Differential Gaussian Matched Filters (DSD-GMF), where each channel is associated with a blood-vessel width. Subsequently, the channel relevance is weighted through the Width Attention Module (WAM), which considers channel and position correlations. Finally, in order to specialize the feature maps with a concrete vessel-width category, either thin-vessel or thick-vessel related, the weighted channels are divided into two groups by the Two-Stream Block, composed of three-level UNet streams. Experimental results on three public datasets (DRIVE/STARE/CHASE) indicate that the proposed method provides a performance gain over other attention-based and non-attention-based architectures, achieving state-of-the-art Accuracy and Area-Under-the-Receiver-Operating-Characteristic-Curve scores (0.9575/0.9665/9653 and 0.9784/0.9865/0.9841 within the Field-of-View, respectively).

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.

Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile.

It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis.

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively.

DilUnet: A U-net based architecture for blood vessels segmentation

Background and objectiveRetinal image segmentation can help clinicians detect pathological disorders by studying changes in retinal blood vessels. This early detection can help prevent blindness and many other vision impairments. So far, several supervised and unsupervised methods have been proposed for the task of automatic blood vessel segmentation. However, the sensitivity and the robustness of these methods can be improved by correctly classifying more vessel pixels. MethodWe proposed an automatic, retinal blood vessel segmentation method based on the U-net architecture. This end-to-end framework utilizes preprocessing and a data augmentation pipeline for training. The architecture utilizes multiscale input and multioutput modules with improved skip connections and the correct use of dilated convolutions for effective feature extraction. In multiscale input, the input image is scaled down and concatenated with the output of convolutional blocks at different points in the encoder path to ensure the feature transfer of the original image. The multioutput module obtains upsampled outputs from each decoder block that are combined to obtain the final output. Skip paths connect each encoder block with the corresponding decoder block, and the whole architecture utilizes different dilation rates to improve the overall feature extraction. ResultsThe proposed method achieved an accuracy: of 0.9680, 0.9694, and 0.9701; sensitivity of 0.8837, 0.8263, and 0.8713; and Intersection Over Union (IOU) of 0.8698, 0.7951, and 0.8184 with three publicly available datasets: DRIVE, STARE, and CHASE, respectively. An ablation study is performed to show the contribution of each proposed module and technique. ConclusionThe evaluation metrics revealed that the performance of the proposed method is higher than that of the original U-net and other U-net-based architectures, as well as many other state-of-the-art segmentation techniques, and that the proposed method is robust to noise.

Multi-step deep neural network for identifying subfascial vessels in a dorsal skinfold window chamber model.

Automatic segmentation of blood vessels in the dorsal skinfold window chamber (DWSC) model is a prerequisite for the evaluation of vascular-targeted photodynamic therapy (V-PDT) biological response. Recently, deep learning methods have been widely applied in blood vessel segmentation, but they have difficulty precisely identifying the subfascial vessels. This study proposed a multi-step deep neural network, named the global attention-Xnet (GA-Xnet) model, to precisely segment subfascial vessels in the DSWC model. We first used Hough transform combined with a U-Net model to extract circular regions of interest for image processing. GA step was then employed to obtain global feature learning followed by coarse segmentation for the entire blood vessel image. Secondly, the coarse segmentation of blood vessel images from the GA step and the same number of retinal images from the DRIVE datasets were combined as the mixing sample, inputted into the Xnet step to learn the multiscale feature predicting fine segmentation maps of blood vessels. The data show that the accuracy, sensitivity, and specificity for the segmentation of multiscale blood vessels in the DSWC model are 96.00%, 86.27%, 96.47%, respectively. As a result, the subfascial vessels could be accurately identified, and the connectedness of the vessel skeleton is well preserved. These findings suggest that the proposed multi-step deep neural network helps evaluate the short-term vascular responses in V-PDT.

Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection

Diabetic retinopathy (DR) has become a major worldwide health problem due to the increase in blindness among diabetics at early ages. The detection of DR pathologies such as microaneurysms, hemorrhages and exudates through advanced computational techniques is of utmost importance in patient health care. New computer vision techniques are needed to improve upon traditional screening of color fundus images. The segmentation of the entire anatomical structure of the retina is a crucial phase in detecting these pathologies. This work proposes a novel framework for fast and fully automatic blood vessel segmentation and fovea detection. The preprocessing method involved both contrast limited adaptive histogram equalization and the brightness preserving dynamic fuzzy histogram equalization algorithms to enhance image contrast and eliminate noise artifacts. Afterwards, the color spaces and their intrinsic components were examined to identify the most suitable color model to reveal the foreground pixels against the entire background. Several samples were then collected and used by the renowned convexity shape prior segmentation algorithm. The proposed methodology achieved an average vasculature segmentation accuracy exceeding 96%, 95%, 98% and 94% for the DRIVE, STARE, HRF and Messidor publicly available datasets, respectively. An additional validation step reached an average accuracy of 94.30% using an in-house dataset provided by the Hospital Sant Joan of Reus (Spain). Moreover, an outstanding detection accuracy of over 98% was achieved for the foveal avascular zone. An extensive state-of-the-art comparison was also conducted. The proposed approach can thus be integrated into daily clinical practice to assist medical experts in the diagnosis of DR.

A novel automatic retinal vessel extraction using maximum entropy based EM algorithm

The extraction of blood vessels helps in the diagnosis of diseases and to develop advances of medicine. Retinal blood vessel extraction plays a crucial role in early detection and treatment of retinal diseases. This paper provides an automatic segmentation of blood vessels in retinal images. First, the fundus images go through preprocessing steps of image acquisition, grey scale conversion, bias correction and adaptive histogram equalization to enhance the appearance of retinal blood vessels. Then the retinal blood vessels are extracted using a probabilistic modeling and maximum entropy based expectation maximization algorithm which uses maximum entropy uniform distribution as the initial condition. The vessels are more accurately confined using image profiles computed perpendicularly across each of the detected vessel centerline. The algorithm is implemented in MATLAB and the performance is tested on retinal images from DRIVE and STARE databases. When validated, we conclude that the segmentation of retinal images using the proposed method shows a sensitivity of 98.9%, a specificity of 83.74%, and an Accuracy score of 98.8%.

Automatic segmentation of blood vessels in retinal images using 2D Gabor wavelet and sub-image thresholding resulting from image partition

The retina features the only blood vessel network in humans that is visible in a non-invasive imaging method. This, along with uniqueness and stability throughout life in healthy subjects, makes it an ideal target for personal identification methods in biometric systems and also for the screening and diagnosis of diseases. However, retinal images usually present low contrast of the vessels in relation to the retinal background and high level of noise stemming mainly from the acquisition process. This work aims to reduce noise and improve contrast to increase the accuracy of retinal vessel segmentation. 2D Gabor wavelet (GW) is usually employed to reduce noise and improve vessel contrast in relation to the background. In this work, it is proposed that, before the thresholding, the GW output images are partitioned into 20 sub-images in such a way that each can be treated independently. The images used were obtained from two public databases, DRIVE and STARE, and the algorithm was developed in MatLab® environment. The proposed approach reached an accuracy of 96.15%, sensitivity of 73.42%, and specificity of 98.30% in DRIVE. In STARE, the accuracy was 94.87%, sensitivity 71.74%, and specificity 96.93%. The methods proposed by the authors indicate gains in accuracy and specificity in the automatic detection of retinal vessels, in both databases used, when compared with those in the main published works. The accuracy is also higher than the 94.73% in interobserver accuracy previously determined as the gold standard.

Multi-channel conditional generative adversarial networks retinal vessel segmentation algorithm

Objective To propose a model for accurately segmenting blood vessels in medical fundus images. Methods The algorithm of deep learning was used for the task of automatic segmentation of blood vessels in retinal fundus images in this paper.An improved vascular segmentation algorithm was proposed.For the different types of blood vessels in the fundus image, a multi-scale network structure was designed to extract features of both main blood vessels and vessel branches at the same time. Results The segmentation model proposed could achieve good results on all kinds of blood vessels even if they have low contrast and few obvious characteristics.The automatic vessel segmentation of retinal fundus images was implemented, and the performance of the model was evaluated through multiple evaluation indexes which are widely used in the field of medical image segmentation in the test stage.A specificity of 0.982 9, an F1 score of 0.794 4, a G-mean of 0.874 8, an Matthews correlation coefficient(MCC) of 0.776 4 and a specificity of 0.978 2 were obtained on the DRIVE dataset.An F1 score of 0.773 5 and an MCC of 0.757 3 were obtained on the STARE data set. Conclusions The proposed method has a great improvement over the segmentation algorithm of the same task.Furthermore, the results generated by our model can achieve comparable effect with the segmentation of human doctor. Key words: Retinal fundus images; Vessel segmentation; Medical image processing; Deep learning; Conditional generative adversarial networks

Effects of Color Constancy Algorithm on Retinal Blood Vessel Segmentation

One of the important steps towards a fully automated retinal image diagnosis system is automatic segmentation of blood vessels from the retinal image. In this paper, we investigate the effects of performing color constancy algorithm on retinal image prior to segmentation step. We compared two different algorithms for color constancy, namely Gray World and White Patch that are applied to retinal image before it is used as an input to the segmentation algorithm. We calculated the performance metrics for the outputs that includes Accuracy, Sensitivity, Specificity and MCC values to compare the performance with and without color constancy applied. From the results, it can be seen that color constancy improves the Sensitivity and MCC value of the segmentation output, with a small decrease in Specificity and Accuracy. Qualitatively, the outputs with color constancy applied have more small vessels, which were mostly left undetected in outputs with no color constancy. These small vessels are one of the key indicators for early detection of diabetic retinopathy.

Automatic Vessel Extraction Using Particle Swarming Optimization for 3D Medical Images

Background: Blood vessel segmentation plays an important role in medical image analysis. Modern blood vessel segmentation algorithms attempts to attempts to increase patient safety by providing better diagnosis and support to more accurate medical decisions. Methods: In 3D image processing techniques leads to an emerging area, and voxel classification. Most of the voxel classification algorithms are a manual classification. This work introduces a novel fully automatic blood vessel segmentation algorithm from 3D images using Hessian-based multi-scale filters (Frangi's filter) and Chan-Vese model with level-set framework. Parameters of Frangi's filter are adjusted by means of an evolutionary computation method, particle swarm optimization (PSO). 3D synthetic and real CTA clinical image database is used to test the proposed algorithm and show a correct voxel classification. Conclusion: The proposed algorithm shows results that are more accurate. Keywords: Particle swarm optimization, Chan-Vese model, level-set, Hessian-based multi-scale filter, 3D medical images, blood vessel segmentation.

Dense concentric circle scanning protocol for measuring pulsatile retinal blood flow in rats with Doppler optical coherence tomography

The variability in the spatial orientation of retinal blood vessels near the optic nerve head (ONH) results in imprecision of the measured Doppler angle and therefore the pulsatile blood flow (BF), when those parameters are evaluated using Doppler OCT imaging protocols based on dual-concentric circular scans. Here, we utilized a dense concentric circle scanning protocol and evaluated its precision for measuring pulsatile retinal BF in rats for different numbers of the circular scans. An spectral domain optical coherence tomography (SD-OCT) system operating in the 1060-nm spectral range with image acquisition rate of 47,000 A-scans/s was used to acquire concentric circular scans centered at the rat's ONH, with diameters ranging from 0.8 to 1.0mm. A custom, automatic blood vessel segmentation algorithm was used to track the spatial orientation of the retinal blood vessels in three dimensions, evaluate the spatially dependent Doppler angle and calculate more accurately the axial BF for each major retinal blood vessel. Metrics such as retinal BF, pulsatility index, and resistance index were evaluated for each and all of the major retinal blood vessels. The performance of the proposed dense concentric circle scanning protocols was compared with that of the dual-circle scanning protocol. Results showed a 3.8±2.2 deg difference in the Doppler angle calculation between the two approaches, which resulted in ∼7% difference in the calculated retinal BF.

Combination of Gray-Level and Moment Invariant for Automatic Blood Vessel Detection on Retinal Image

Segmentation of blood vessels in the retinal is a crucial step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. This paper presents a supervised method for automatic segmentation of blood vessels in retinal images. The proposed method based on a hybrid combination between Gray-Level and Moment Invariant techniques. There are four steps involved, whereas preprocessing, feature extraction, classification, and post-processing. In the preprocessing, three stages are performed include vessel central light reflex removal, background homogenization, and vessel enhancement. The 7-D vector feature extraction was performed to compute that compose of gray-level and moment invariants-based features for pixel representation. The decision tree is used for classification step that characterized the pixel based on vessels and non-vessels. The final step is the post-processing which will remove the small artifacts appears after classification process. The proposed method was compared to the Vascular Tree method and Morphological method. Based on the objective evaluation, the proposed method achieved (sensitivity = 98.589, specificity = 55.544 and accuracy = 96.197).

Morphological statistical features for automatic segmentation of blood vessel structure in retinal images

This paper presents a new method for automatic segmentation of blood vessels in retinal images. The proposed method is based on morphological high pass filter called top hat transform and statistical features. Directional information of vasculature structure is captured using top hat transform with line structuring element rotated at 18 different directions. Statistical features namely mean, median, maximum and variance are used to reconstruct the enhanced vasculature structure. The proposed method obtained four enhanced vasculature structure based on these statistical features. Best statistical feature is determined by evaluating the segmentation performance in terms of accuracy, sensitivity and specificity. This method is evaluated on the publicly available DRIVE database. Segmentation results show that the method outperforms all evaluated segmentation approaches. Its accuracy and fast execution make it appropriate for automatic screening system of retina related diseases such as diabetic retinopathy.