scholarly journals Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image

2020 ◽  
Vol 10 (11) ◽  
pp. 3777 ◽  
Author(s):  
Yun Jiang ◽  
Falin Wang ◽  
Jing Gao ◽  
Simin Cao
Keyword(s):  
Blood Vessel ◽  
Optic Disc ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetes can induce diseases including diabetic retinopathy, cataracts, glaucoma, etc. The blindness caused by these diseases is irreversible. Early analysis of retinal fundus images, including optic disc and optic cup detection and retinal blood vessel segmentation, can effectively identify these diseases. The existing methods lack sufficient discrimination power for the fundus image and are easily affected by pathological regions. This paper proposes a novel multi-path recurrent U-Net architecture to achieve the segmentation of retinal fundus images. The effectiveness of the proposed network structure was proved by two segmentation tasks: optic disc and optic cup segmentation and retinal vessel segmentation. Our method achieved state-of-the-art results in the segmentation of the Drishti-GS1 dataset. Regarding optic disc segmentation, the accuracy and Dice values reached 0.9967 and 0.9817, respectively; as regards optic cup segmentation, the accuracy and Dice values reached 0.9950 and 0.8921, respectively. Our proposed method was also verified on the retinal blood vessel segmentation dataset DRIVE and achieved a good accuracy rate.

scholarly journals Retinal blood vessel segmentation based on heuristic image analysis

2019 ◽  
Vol 16 (1) ◽  
pp. 227-245 ◽  
Author(s):  
Maja Braovic ◽  
Darko Stipanicev ◽  
Ljiljana Seric
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Vessel Segmentation ◽  
Optic Disk ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

scholarly journals Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy

2018 ◽  
Vol 7 (3.18) ◽  
pp. 16
Author(s):  
Kuryati Kipli ◽  
Cripen Jiris ◽  
Siti Kudnie Sahari ◽  
Rohan Sapawi ◽  
Nazreen Junaidi ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Image Database ◽  
Vessel Segmentation ◽  
Fundus Image ◽  
Segmentation Method ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Automated Method ◽  
Arteriolar Narrowing ◽  
Retinal Fundus

Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s Thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). The developed segmentation method accuracies are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma. 

scholarly journals Retinal blood vessel segmentation from fundus image using an efficient multiscale directional representation technique Bendlets

2020 ◽  
Vol 17 (6) ◽  
pp. 7751-7771
Author(s):  
Rafsanjany Kushol ◽  
◽  
Md. Hasanul Kabir ◽  
M. Abdullah-Al-Wadud ◽  
Md Saiful Islam ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Fundus Image ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Representation Technique
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