Extraction of retinal vasculature by using morphology in fundus images

2015 ◽  
Author(s):  
Namita Sengar ◽  
Malay Kishore Dutta ◽  
M. Parthasarthi ◽  
Radim Burget
Keyword(s):  
Retinal Vasculature ◽  
Fundus Images

scholarly journals Hemodynamics in the retinal vasculature during the progression of diabetic retinopathy

2017 ◽  
Vol 1 (4) ◽  
pp. 6-15
Author(s):  
Francesco Calivá ◽  
Georgios Leontidis ◽  
Piotr Chudzik ◽  
Andrew Hunter ◽  
Luca Antiga ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Blood Flow ◽  
Diabetic Retinopathy ◽  
Fluid Dynamic ◽  
First Year ◽  
Retinal Vasculature ◽  
Fundus Images ◽  
Parent Vessel ◽  
Last Stage ◽  
Vascular Trees

Purpose: In this study, it is shown that hemodynamic features are applicable as biomarkers to evaluate the progression of diabetic retinopathy (DR). Methods: Ninety-six fundus images from twenty-four subjects were selected. For each patient, four photographs were captured during the three years before DR and in the first year of DR. The vascular trees, which consisted of a parent vessel and two child branches were extracted, and at the branching nodes, the fluid dynamic conditions were estimated. Results: Veins were mostly affected during the last stage of diabetes before DR. In the arteries, the blood flow in both child branches and the Reynolds number in the smaller child branch were mostly affected. Conclusion: This study showed that hemodynamic features can add further information to the study of the progression of DR.

Morphometric Measurements of The Retinal Vasculature in Fundus Images with Vampire

2015 ◽  
pp. 91-111 ◽  
Author(s):  
Emanuele Trucco ◽  
Andrea Giachetti ◽  
Lucia Ballerini ◽  
Devanjali Relan ◽  
Alessandro Cavinato ◽  
...  
Keyword(s):  
Retinal Vasculature ◽  
Fundus Images ◽  
Morphometric Measurements

Automated Model-Based Segmentation, Tracing, and Analysis of Retinal Vasculature from Digital Fundus Images

2003 ◽  
pp. 225-297 ◽  
Author(s):  
Ali Can ◽  
Howard Tanenbaum ◽  
Chia-Ling Tsai ◽  
Charles Stewart ◽  
Hong Shen ◽  
...  
Keyword(s):  
Retinal Vasculature ◽  
Fundus Images ◽  
Model Based

scholarly journals Noninvasive temporal detection of early retinal vascular changes during diabetes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohammad Ali Saghiri ◽  
Andrew Suscha ◽  
Shoujian Wang ◽  
Ali Mohammad Saghiri ◽  
Christine M. Sorenson ◽  
...  
Keyword(s):  
Disease Progression ◽  
Imaging System ◽  
Therapeutic Interventions ◽  
Retinal Vasculature ◽  
Fundus Images ◽  
Diabetic Mice ◽  
Vascular Changes ◽  
Duration Of Diabetes ◽  
Microvascular Changes ◽  
Canny Edge

Abstract Diabetes associated complications, including diabetic retinopathy and loss of vision, are major health concerns. Detecting early retinal vascular changes during diabetes is not well documented, and only few studies have addressed this domain. The purpose of this study was to noninvasively evaluate temporal changes in retinal vasculature at very early stages of diabetes using fundus images from preclinical models of diabetes. Non-diabetic and Akita/+ male mice with different duration of diabetes were subjected to fundus imaging using a Micron III imaging system. The images were obtained from 4 weeks- (onset of diabetes), 8 weeks-, 16 weeks-, and 24 weeks-old male Akita/+ and non-diabetic mice. In total 104 fundus images were subjected to analysis for various feature extractions. A combination of Canny Edge Detector and Angiogenesis Analyzer plug-ins in ImageJ were utilized to quantify various retinal vascular changes in fundus images. Statistical analyses were conducted to determine significant differences in the various extracted features from fundus images of diabetic and non-diabetic animals. Our novel image analysis method led to extraction of over 20 features. These results indicated that some of these features were significantly changed with a short duration of diabetes, and others remained the same but changed after longer duration of diabetes. These patterns likely distinguish acute (protective) and chronic (damaging) associated changes with diabetes. We show that with a combination of various plugging one can extract over 20 features from retinal vasculature fundus images. These features change during diabetes, thus allowing the quantification of quality of retinal vascular architecture as biomarkers for disease progression. In addition, our method was able to identify unique differences among diabetic mice with different duration of diabetes. The ability to noninvasively detect temporal retinal vascular changes during diabetes could lead to identification of specific markers important in the development and progression of diabetes mediated-microvascular changes, evaluation of therapeutic interventions, and eventual reversal of these changes in order to stop or delay disease progression.

scholarly journals Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures

2021 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Muhammad Arsalan ◽  
Adnan Haider ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Experimental Results ◽  
Health Study ◽  
Retinal Vasculature ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Retinal Disorders ◽  
Structured Analysis ◽  
Vessel Extraction

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method’s performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

Segmentation of retinal vasculature in colour fundus images

2009 ◽  
Author(s):  
Kee Yong Pang ◽  
I. Lila Iznita ◽  
M. H. Ahmad Fadzil ◽  
A. N. Hanung ◽  
N. Hermawan ◽  
...  
Keyword(s):  
Retinal Vasculature ◽  
Fundus Images
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