scholarly journals Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6380
Author(s):  
Alexander Ze Hwan Ooi ◽  
Zunaina Embong ◽  
Aini Ismafairus Abd Hamid ◽  
Rafidah Zainon ◽  
Shir Li Wang ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Edge Detection ◽  
Vessel Segmentation ◽  
Fundus Photography ◽  
Fundus Image ◽  
Detection Techniques ◽  
Blood Vessel Segmentation ◽  
Retinal Fundus Image ◽  
Canny Edge ◽  
Retinal Fundus

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.

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 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 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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