Retinal Blood Vessel Segmentation Based on Multi-Scale Deep Learning

Li, Ming; Yin, Qinye; Lu, Mingyu

doi:10.15439/2018f127

Cited by 30 publications

(20 citation statements)

References 21 publications

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“…Li et al [73] proposed the method of analyzing fundus images by segmentation of retinal vessels, based on supervised vessel segmentation by the deep learning method. They found the problem of the imbalance of the retinal vessels which limits the improvement of the accuracy of the segmentation.…”

Section: A Analysis Methodology Of Existing Deep Learning Methods Fomentioning

confidence: 99%

“…Li et al [73] used a database containing retinal fundus images of 5620 patients to validate their method for segmentation of the retinal blood vessels. The proposed method achieved the optimum accuracy of 0.949, but tiny vessels are missing, as shown in Figure 21, just as they have not compared their method to other existing methods.…”

Section: B Analysis Performance Of Existing Deep Learning Methods Fomentioning

confidence: 99%

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Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Soomro¹,

et al. 2019

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This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. The ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision-based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular, deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis. INDEX TERMS Retinal colour fundus images, convolutional neural networks, retinal vessels segmentation.

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Section: A Analysis Methodology Of Existing Deep Learning Methods Fomentioning

confidence: 99%

Section: B Analysis Performance Of Existing Deep Learning Methods Fomentioning

confidence: 99%

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Soomro¹,

et al. 2019

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“…Some notable examples are diagnoses of the glaucoma, the retinal vascular occlusions and the DR [32], [33], [4], [34], [35]. Although the supervised machine learning approaches have achieved significantly better evaluation metrics for various tasks such as vessels detection and optic cup/disc detection [9], [10], [7], [8] but unsupervised machine learning approaches are still being explored and preferred because of their lower computational complexity and no requirement of manually marked retinal image databases, in addition to many constraints of the supervised machine learning approaches.…”

Section: Related Workmentioning

confidence: 99%

“…The efficient implementation of these automated system is crucial for the early detection of numerous diseases. The implementation of these automated system involves various tasks such as the detection of optic disc/cup [7], [8] and retinal vessels [9], [10]. The performance and reliability of the automated public screening system is substantially important because of the health of the general public.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

2020

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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 denoise 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.

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“…Li et al framed a supervised vascular segmentation approach for retinal fundus images using multi-scale convolutional neural networks. They have also used the label processing approach to achieve better segmentation accuracy [38]. Dasgupta et al formulated the retinal vessels segmentation task as a multi-label inference task, which includes the convolutional neural network and structured prediction [39].…”

Section: Related Workmentioning

confidence: 99%

Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

et al. 2019

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A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for images of different eye-related diseases and the degradation of the vessels differ frompatient to patient. If the blood vessels are extracted from the fundus images, it will make thediagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithmexclusively for the extraction of blood vessels from the fundus image. The proposed algorithm ishybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement(MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, thearea-based morphological operator is applied to highlight the blood vessels. To validate theproposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus(HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments theblood vessels with more accuracy than the existing algorithms.

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Retinal Blood Vessel Segmentation Based on Multi-Scale Deep Learning

Cited by 30 publications

References 21 publications

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

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