New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation

FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation

Accurate and automated retinal vessel segmentation is essential for performing diagnosis and surgical planning of retinal diseases. However, conventional U-shaped networks often suffer from segmentation errors when dealing with fine and low-contrast blood vessels due to the loss of continuous resolution in the encoding stage and the inability to recover the lost information in the decoding stage. To address this issue, this paper introduces an effective full-resolution retinal vessel segmentation network, namely FRD-Net, which consists of two core components: the backbone network and the multi-scale feature fusion module (MFFM). The backbone network achieves horizontal and vertical expansion through the interaction mechanism of multi-resolution dilated convolutions while preserving the complete image resolution. In the backbone network, the effective application of dilated convolutions with varying dilation rates, coupled with the utilization of dilated residual modules for integrating multi-scale feature maps from adjacent stages, facilitates continuous learning of multi-scale features to enhance high-level contextual information. Moreover, MFFM further enhances segmentation by fusing deeper multi-scale features with the original image, facilitating edge detail recovery for accurate vessel segmentation. In tests on multiple classical datasets,compared to state-of-the-art segmentation algorithms, FRD-Net achieves superior performance and generalization with fewer model parameters.

A Review on Retinal Blood Vessel Enhancement and Segmentation Techniques for Color Fundus Photography

The retinal image is a trusted modality in biomedical image-based diagnosis of many ophthalmologic and cardiovascular diseases. Periodic examination of the retina can help in spotting these abnormalities in the early stage. However, to deal with today's large population, computerized retinal image analysis is preferred over manual inspection. The precise extraction of the retinal vessel is the first and decisive step for clinical applications. Every year, many more articles are added to the literature that describe new algorithms for the problem at hand. The majority of the review article is restricted to a fairly small number of approaches, assessment indices, and databases. In this context, a comprehensive review of different vessel extraction methods is inevitable. It includes the development of a first-hand classification of these methods. A bibliometric analysis of these articles is also presented. The benefits and drawbacks of the most commonly used techniques are summarized. The primary challenges, as well as the scope of possible changes, are discussed. In order to make a fair comparison, numerous assessment indices are considered. The findings of this survey could provide a new path for researchers for further work in this domain.

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.

An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf

Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4 s, which has been identified on boundary condition of Pdf function.

A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers

In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise.

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients' eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients' retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for vessel segmentation and classification for fundus camera images.

Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition

The feature extraction stage remains a major component of every biometric recognition system. In most instances, the eventual accuracy of a recognition system is dependent on the features extracted from the biometric trait and the feature extraction technique adopted. The widely adopted technique employs features extracted from healthy retinal images in training retina recognition system. However, literature has shown that certain eye diseases such as diabetic retinopathy (DR), hypertensive retinopathy, glaucoma, and cataract could alter the recognition accuracy of the retina recognition system. This connotes that a robust retina recognition system should be designed to accommodate healthy and diseased retinal images. A framework with two different approaches for retina image recognition is presented in this study. The first approach employed structural features for healthy retinal image recognition while the second employed vascular and lesion-based features for DR retinal image recognition. Any input retinal image was first examined for the presence of DR symptoms before the appropriate feature extraction technique was adopted. Recognition rates of 100% and 97.23% were achieved for the healthy and DR retinal images, respectively, and a false acceptance rate of 0.0444 and a false rejection rate of 0.0133 were also achieved.

Retinal Blood Vessel Extraction From Fundus Images Using Improved Otsu Method

In the present time, the identification of blood vessels is a basic task for diagnosis of various eye abnormalities. So, this article offers an instinctive approach for identification of blood vessels in ophthalmoscope images. This approach includes three different phases: pre-processing, vessel extraction and post-processing for getting a final vessel segmentation outcome. In the presented method, formerly log transformation and contrast limited adaptive histogram equalization are used for the enhancement of retinal images. The enhanced image is then filtered using a morphological opening operation and subsequently the optic disk is removed. The second phase includes the application of the improved Otsu method on the pre-processed image for the identification of blood vessels. Lastly, the resultant vessel-segmented image is obtained by using the morphological cleaning operation. The proposed method is fast, time efficient, and gives consistent accuracy for all retinal images. It is more robust and easier to implement compared to other traditional methods. The performance of the presented method is evaluated using ten different mathematical measures. It achieves average sensitivity, specificity and accuracy of 0.710, 0.982 and 0.956 for the digital retinal images for vessel extraction (DRIVE) database, 0.738, 0.982 and 0.954 for the structure analysis of the retina (STARE) database and 0.737, 0.964 and 0.949 for the child heart and health study in England (CHASE_DB1) database. The presented method also performs better in segmenting thin vessels by giving average accuracies of 0.964, 0.954 and 0.965 for DRIVE, STARE and CHASE_DB1 databases respectively.

Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma

Glaucoma is a progressive optic neuropathy characterized by peripheral visual field loss, which is caused by degeneration of retinal nerve fibers. The peripheral vision loss due to glaucoma is asymptomatic. If not detected and treated at an early stage, it leads to complete blindness, which is irreversible in nature. The retinal nerve fiber layer defect (RNFLD) provides an earliest objective evidence of glaucoma. In this regard, we explore cost-effective redfree fundus imaging for RNFLD detection to be practically useful for computer-assisted early glaucoma risk assessment. RNFLD appears as a wedge shaped arcuate structure radiating from the optic disc. The very low contrast between RNFLD and background makes its visual detection quite challenging even by medical experts. In our study, we formulate a deep convolutional neural network (CNN) based patch classification strategy for RNFLD boundary localization. A large number of RNFLD and background image patches train the deep CNN model, which extracts sufficient discriminative information from the patches and results in accurate RNFLD boundary pixel classification. The proposed approach is found to achieve enhanced RNFLD detection performance with sensitivity of 0.8205 and false positive per image of 0.2000 on a newly created early glaucomatic fundus image database.

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images

Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.

Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Tsallis entropy and sparse reconstructive dictionary learning for exudate detection in diabetic retinopathy

Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding. The obtained candidate exudate pixel map is useful for further removal of falsely detected candidates using sparse-based dictionary learning and classification. Two reconstructive dictionaries are learnt using the intensity, gradient, local energy, and transform domain features extracted from exudate and background patches of the training fundus images. Then, a sparse representation-based classifier separates the true exudate pixels from false positives using least reconstruction error. The proposed method is evaluated on the publicly available e-ophtha EX and standard DR database calibration level 1 (DIARETDB1) databases and high exudate detection performance is achieved. In the e-ophtha EX database, mean sensitivity of 85.80% and positive predictive value of 57.93% are found. For the DIARETDB1 database, an area under the curve of 0.954 is obtained.