Retinal vasculature segmentation and measurement framework for color fundus and SLO images

Automatic Method for Blood Vessel Diameter Measurement on Color Retinal Images

Automatic detection of retinal blood vessels and measurement of blood vessel diameter has become very useful in medical applications, including the diagnosis and monitoring of vascular therapy. This paper present a new method for the detection of retinal blood vessels from fundus images. In addition, it accurately determines the blood vessels diameter. The proposed system consists of several stages starting from the detection of a complete blood vessel network (retinal images), a segmentation process, an extraction of blood vessel centerlines, and a new approach for the measurement of the blood vessel diameters utilizing the circle diameter approach. The application of the proposed method is based on three publicly available databases (DRIVE, STARE, CHASE_DB and HRF), which has been conducted and compared to recently reported measurement techniques. Numerical results demonstrate the efficacy of the proposed method achieving a success rate of 100% with 2.2454×10−4 as mean and 4.5833×10−5 as standard deviation differences. This could be harnessed in the future blood vessel measurement techniques.

Retinal Vessel Segmentation, a Review of Classic and Deep Methods

Retinal illnesses such as diabetic retinopathy (DR) are the main causes of vision loss. In the early recognition of eye diseases, the segmentation of blood vessels in retina images plays an important role. Different symptoms of ocular diseases can be identified by the geometric features of ocular arteries. However, due to the complex construction of the blood vessels and their different thicknesses, segmenting the retina image is a challenging task. There are a number of algorithms that helped the detection of retinal diseases. This paper presents an overview of papers from 2016 to 2022 that discuss machine learning and deep learning methods for automatic vessel segmentation. The methods are divided into two groups: Deep learning-based, and classic methods. Algorithms, classifiers, pre-processing and specific techniques of each group is described, comprehensively. The performances of recent works are compared based on their achieved accuracy in different datasets in inclusive tables. A survey of most popular datasets like DRIVE, STARE, HRF and CHASE_DB1 is also given in this paper. Finally, a list of findings from this review is presented in the conclusion section.

Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Fundus imaging is one of the crucial methods that help ophthalmologists for diagnosing the various eye diseases in modern medicine. An accurate vessel segmentation method can be a convenient tool to foresee and analyze fatal diseases, including hypertension or diabetes, which damage the retinal vessel’s appearance. This work suggests an unsupervised approach for vessels segmentation out of retinal images. The proposed method includes multiple steps. Firstly, from the colored retinal image, green channel is extracted and preprocessed utilizing Contrast Limited Histogram Equalization as well as Fuzzy Histogram Based Equalization for contrast enhancement. To expel geometrical articles (macula, optic disk) and noise, top-hat morphological operations are used. On the resulted enhanced image, matched filter and Gabor wavelet filter are applied, and the outputs from both is added to extract vessels pixels. The resulting image with the now noticeable blood vessel is binarized using human visual system (HVS). A final image of segmented blood vessel is obtained by applying post-processing. The suggested method is assessed on two public datasets (DRIVE and STARE) and showed comparable results with regard to sensitivity, specificity and accuracy. The results we achieved with respect to sensitivity, specificity together with accuracy on DRIVE database are 0.7271, 0.9798 and 0.9573, and on STARE database these are 0.7164, 0.9760, and 0.9560, respectively, in less than 3.17 s on average per image.

Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

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

BACKGROUND AND OBJECTIVE

Automatic segmentation of retinal blood vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick retinal vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature.

METHODS

The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage.

RESULTS

The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset.

CONCLUSIONS

The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin vessels was possible with the proposed approach when compared with other procedures.