Retinal Blood Vessel Segmentation Method Research Articles

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation

As an important basis of clinical diagnosis, the morphology of retinal vessels is very useful for the early diagnosis of some eye diseases. In recent years, with the rapid development of deep learning technology, automatic segmentation methods based on it have made considerable progresses in the field of retinal blood vessel segmentation. However, due to the complexity of vessel structure and the poor quality of some images, retinal vessel segmentation, especially the segmentation of Capillaries, is still a challenging task. In this work, we propose a new retinal blood vessel segmentation method, called multi-feature segmentation, based on collaborative patches. First, we design a new collaborative patch training method which effectively compensates for the pixel information loss in the patch extraction through information transmission between collaborative patches. Additionally, the collaborative patch training strategy can simultaneously have the characteristics of low occupancy, easy structure and high accuracy. Then, we design a multi-feature network to gather a variety of information features. The hierarchical network structure, together with the integration of the adaptive coordinate attention module and the gated self-attention module, enables these rich information features to be used for segmentation. Finally, we evaluate the proposed method on two public datasets, namely DRIVE and STARE, and compare the results of our method with those of other nine advanced methods. The results show that our method outperforms other existing methods.

Dual-channel asymmetric convolutional neural network for an efficient retinal blood vessel segmentation in eye fundus images

The morphological properties of retinal vessels are closely related to the diagnosis of ophthalmic diseases. However, many problems in retinal images, such as complicated directions of vessels and difficult recognition of capillaries, bring challenges to the accurate segmentation of retinal blood vessels. Thus, we propose a new retinal blood vessel segmentation method based on a dual-channel asymmetric convolutional neural network (CNN). First, we construct the thick and thin vessel extraction module based on the morphological differences in retinal vessels. A two-dimensional (2D) Gabor filter is used to perceive the scale characteristics of blood vessels after selecting the direction of blood vessels; thereby, adaptively extracting the thick vessel features characterizing the overall characteristics and the thin vessel features preserving the capillaries from fundus images. Then, considering that the single-channel network is unsuitable for the unified characterization of thick and thin vessels, we develop a dual-channel asymmetric CNN based on the U-Net model. The MainSegment-Net uses the step-by-step connection mode to achieve rapid positioning and segmentation of thick vessels; the FineSegment-Net combines dilated convolution and the skip connection to achieve the fine extraction of thin vessels. Finally, the output of the dual-channel asymmetric CNN is fused and coded to combine the segmentation results of thick and thin vessels. The performance of our method is evaluated and tested by DRIVE and CHASE_DB1. The results show that the accuracy (Acc), sensitivity (SE), and specificity (SP) of our method on the DRIVE database are 0.9630, 0.8745, and 0.9823, respectively. The evaluation indexes Acc, SE, and SP of the CHASE_DB1 database are 0.9694, 0.8916, and 0.9794, respectively. Additionally, our method combines the biological vision mechanism with deep learning to achieve rapid and automatic segmentation of retinal vessels, providing a new idea for diagnosing and analyzing subsequent medical images.

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.

Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE_DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method.

A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions

Recent advancements in deep learning architectures have extended their application to computer vision tasks, one of which is the segmentation of retinal blood vessels from retinal fundus images. This is a problem that has piqued researchers’ interest in recent times. This paper presents a review of the taxonomy and analysis of enhancement techniques used in recent works to modify and optimize the performance of deep learning retinal blood vessels segmentation methods. The objectives of this study are to critically review the taxonomies of the state-of-the-art deep learning retinal blood vessels segmentation methods, observe the trends of the enhancement techniques of recent work, identify the challenges, and suggest potential future research directions. The taxonomies focused on in this paper include optimization algorithms, regularization methods, pooling operations, activation functions, transfer learning, and ensemble learning methods. In doing this, 110 relevant papers spanning the years 2016 to 2021 are reviewed. The findings could aid future research plans, while the suggested ideas would improve the predictive accuracy of future models for automatic retinal blood vessels segmentation algorithms with good generalization ability and optimal performance.

Fast and efficient retinal blood vessel segmentation method based on deep learning network.

The segmentation of the retinal vascular tree presents a major step for detecting ocular pathologies. The clinical context expects higher segmentation performance with a reduced processing time. For higher accurate segmentation, several automated methods have been based on Deep Learning (DL) networks. However, the used convolutional layers bring to a higher computational complexity and so for execution times. For such need, this work presents a new DL based method for retinal vessel tree segmentation. Our main contribution consists in suggesting a new U-form DL architecture using lightweight convolution blocks in order to preserve a higher segmentation performance while reducing the computational complexity. As a second main contribution, preprocessing and data augmentation steps are proposed with respect to the retinal image and blood vessel characteristics. The proposed method is tested on DRIVE and STARE databases, which can achieve a better trade-off between the retinal blood vessel detection rate and the detection time with average accuracy of 0.978 and 0.98 in 0.59 s and 0.48 s per fundus image on GPU NVIDIA GTX 980 platforms, respectively for DRIVE and STARE database fundus images.

Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement.

Objective:Aiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed.Methods:The multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure.Results:Experiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively.Conclusion:The segmentation accuracy is high, and better blood vessel segmentation results can be obtained.

An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction.

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

Enhancing detection of retinal blood vessels by combined approach of DWT, Tyler Coye and Gamma correction

Retinal imaging plays the most significant roles among all the medical imaging technology, because of its ability to derive various features that is related to various eye diseases. Thus, precise extraction is highly required for the blood vessels that lead to accurate identification of the diseases. Because of some instabilities in the retinal images such as noise, non-uniform illumination etc. it may motivate towards a false detection. This paper suggests an automated method to extract the blood vessels from fundus by integrating discrete wavelet transform (DWT) with Tyler Coye algorithm. Furthermore, in the retinal blood vessel segmentation methods the most vital step is to enhance the contrast. The authenticity of the segmentation is purely subjected to the uniformity of the contrast over the image. Thus, an illumination normalization technique known as Gamma correction is also utilized for the contrast enhancement. Three different types of experiments are carried out: original Tyler Coye algorithm, DWT with different wavelet filters integrated with Tyler Coye, and Gamma correction normalization applied to the integrated model. The approaches are tested with DRIVE dataset. The performance of the normalization-integrated model is found superior to the other two approaches.

GENOMICS OF PSYCHIATRIC RESILIENCE: NOVEL APPROACHES AND FINDINGS

Retinal imaging plays the most significant roles among all the medical imaging technology, because of its ability to derive various features that is related to various eye diseases. Thus, precise extraction is highly required for the blood vessels that lead to accurate identification of the diseases. Because of some instabilities in the retinal images such as noise, non-uniform illumination etc. it may motivate towards a false detection. This paper suggests an automated method to extract the blood vessels from fundus by integrating discrete wavelet transform (DWT) with Tyler Coye algorithm. Furthermore, in the retinal blood vessel segmentation methods the most vital step is to enhance the contrast. The authenticity of the segmentation is purely subjected to the uniformity of the contrast over the image. Thus, an illumination normalization technique known as Gamma correction is also utilized for the contrast enhancement. Three different types of experiments are carried out: original Tyler Coye algorithm, DWT with different wavelet filters integrated with Tyler Coye, and Gamma correction normalization applied to the integrated model. The approaches are tested with DRIVE dataset. The performance of the normalization-integrated model is found superior to the other two approaches.

Performance comparison of publicly available retinal blood vessel segmentation methods

Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels.Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods by the linear regression model is tested for each database. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors.The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

A new supervised retinal vessel segmentation method based on robust hybrid features

Abstract In this paper, we propose a new supervised retinal blood vessel segmentation method that combines a set of very robust features from different algorithms into a hybrid feature vector for pixel characterization. This 17-D feature vector consists of 13 Gabor filter responses computed at different configurations, contrast enhanced intensity, morphological top-hat transformed intensity, vesselness measure, and B-COSFIRE filter response. A random forest classifier, known for its speed, simplicity, and information fusion capability, is trained with the hybrid feature vector. The chosen combination of the different types of individually strong features results in increased local information with better discrimination for vessel and non-vessel pixels in both healthy and pathological retinal images. The proposed method is evaluated in detail on two publicly available databases DRIVE and STARE. Average classification accuracies of 0.9513 and 0.9605 on the DRIVE and STARE datasets, respectively, are achieved. When the majority of the common performance metrics are considered, our method is superior to the state-of-the-art methods. Performance results show that our method also outperforms the state-of-the-art methods in both cross training and pathological cases.

An automated retinal imaging method for the early diagnosis of diabetic retinopathy

Diabetic retinopathy is a microvascular complication of long-term diabetes and is the major cause for eyesight loss due to changes in blood vessels of the retina. Major vision loss due to diabetic retinopathy is highly preventable with regular screening and timely intervention at the earlier stages. Retinal blood vessel segmentation methods help to identify the successive stages of such sight threatening diseases like diabetes. To develop and test a novel retinal imaging method which segments the blood vessels automatically from retinal images, which helps the ophthalmologists in the diagnosis and follow-up of diabetic retinopathy. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels were identified by means of a multilayer perceptron neural network, for which the inputs were derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network, is utilized in our method. Quantitative results of sensitivity, specificity and predictive values were obtained in our method and the measured accuracy of our segmentation algorithm was 95.3%, which is better than that presented by state-of-the-art approaches. The evaluation procedure used and the demonstrated effectiveness of our automated retinal imaging method proves itself as the most powerful tool to diagnose diabetic retinopathy in the earlier stages.

Retinal Blood Vessel Segmentation with Optic Disc Pixels Exclusion

The morphological changes of retinal blood vessels are important indicators used to diagnose and monitor the progression of various diseases. A number of retinal blood vessel segmentation methods have been introduced, including the line operator based methods, which have shown satisfactory results. However, the basic line operator method cannot discriminate the pixels around the retinal optic disc, resulting in false classification of those pixels. In this paper, we integrate the detection of pixels around the retinal optic disc to the line operator method so that those pixels can be excluded from the vessel pixels. The method is evaluated on the widely used retinal dataset, the DRIVE dataset. The results demonstrate that the proposed method has made improvements over the basic and the multi-scale line detector with accuracy and area under curve of 0.942 and 0.9521, respectively.

An effective retinal blood vessel segmentation method using multi-scale line detection

Changes in retinal blood vessel features are precursors of serious diseases such as cardiovascular disease and stroke. Therefore, analysis of retinal vascular features can assist in detecting these changes and allow the patient to take action while the disease is still in its early stages. Automation of this process would help to reduce the cost associated with trained graders and remove the issue of inconsistency introduced by manual grading. Among different retinal analysis tasks, retinal blood vessel extraction plays an extremely important role as it is the first essential step before any measurement can be made. In this paper, we present an effective method for automatically extracting blood vessels from colour retinal images. The proposed method is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved. To maintain the strength and eliminate the drawbacks of each individual line detector, the line responses at varying scales are linearly combined to produce the final segmentation for each retinal image. The performance of the proposed method was evaluated both quantitatively and qualitatively on three publicly available DRIVE, STARE, and REVIEW datasets. On DRIVE and STARE datasets, the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) while retaining comparable accuracy compared to other existing methods. Visual inspection on the segmentation results shows that the proposed method produces accurate segmentation on central reflex vessels while keeping close vessels well separated. On REVIEW dataset, the vessel width measurements obtained using the segmentations produced by the proposed method are highly accurate and close to the measurements provided by the experts. This has demonstrated the high segmentation accuracy of the proposed method and its applicability for automatic vascular calibre measurement. Other advantages of the proposed method include its efficiency with fast segmentation time, its simplicity and scalability to deal with high resolution retinal images.