scholarly journals An Effective Approach for Blood Vessel Tree Extraction from Retinal Images with Adaptive Weighted Contrast Adjustment Approach

2019 ◽  
Vol 8 (2) ◽  
pp. 285-292
Author(s):  
Shafiulla Basha s ◽  
◽  
Venkata Ramanaiah K ◽  
Keyword(s):  
Blood Vessel ◽  
Retinal Images ◽  
Vessel Tree ◽  
Tree Extraction

A Comparative Study of Different Blood Vessel Detection on Retinal Images

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Wan Azani Mustafa ◽  
◽  
Ahmad Syauqi Mahmud ◽  
Muhammad Zaid Aihsan ◽  
M. Saifizi ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Comparative Study ◽  
Retinal Images ◽  
Blood Vessel Detection ◽  
Vessel Detection

scholarly journals Extraction of Blood Vessels in Retinal Images Using Four Different Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Blood Vessel ◽  
Cross Sections ◽  
Retinal Images ◽  
Matched Filtering ◽  
Edge Enhancement ◽  
Promising Technique ◽  
Filtering Technique ◽  
Continuation Algorithm ◽  
Line Cross ◽  
Vessel Extraction

A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique.

scholarly journals Reliability of Manual Vascular Segmentation for Retinal Fractal Dimension using Peek Retinatm

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1560-1564
Keyword(s):  
Fractal Dimension ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Retinal Image ◽  
Retinal Images ◽  
Retinal Vasculature ◽  
Manual Segmentation ◽  
Good Reliability ◽  
Fundus Camera ◽  
Df Analysis

Fractal dimension (Df) has been identified as indirect measure in quantifying the complexity of retinal vessel network which is useful for early detection of vascular changes. Reliability studies of Df measurement on retinal vasculature, has been conducted on retinal images processed by using semi-automated software which only permits image with 45ᵒ field of view (FOV). Smartphone-assisted fundus camera retinal image has a maximum 30ᵒ FOV which warrant manual processing in measuring the Df. Retinal blood vessels need to be manually segmented to produce binary images for retinal vasculatures Df measurement. Therefore, this study was conducted to determine the intragrader and intergrader reliability of manual segmentation of the retinal vasculature Df measurement from retinal images taken using a smartphone-assisted fundus camera Forty-five retinal images were captured using the Portable Eye Examination Kit Retina (Peek Retina™, Peek Vision Ltd, UK). Suitable image for Df analysis were selected based on gradable retinal image criteria which included; i) good image focus, ii) centered position of optic nerve head (ONH) and iii) significant blood vessel visibility. The images were cropped 0.5 disc diameters away from disc margin and resized to 500x500 pixels using GNU Image Manipulation Program Version 2.8.18 (GIMP, The GIMP Team, United States). Retinal vessels were manually traced by using layering capabilities for blood vessel segmentation. Df values of segmented blood vessels were measured by using Image J (National Institutes of Health, USA) and its plugin software, FracLac Version 2.5. Intragrader and intergrader reliability was determined by comparing the Df values between; two readings measured one week apart by a grader and readings from two different graders, respectively, using intraclass correlation coefficient (ICC) and Bland-Altman graphical plots. Intragrader agreement for retinal Df showed good reliability with ICC of 0.899 (95% CI: 0.814–0.945). Bland Altman analysis indicated good agreement between Df values at different grading time (mean difference 0.0050; 95% CI:-0.0001–0.0101). Intergrader reliability for retinal Df was high with ICC of 0.814 (95% CI: 0.459–0.919). Bland Altman plot revealed good intergrader agreement for retinal Df between two graders with a bias value of 0.0158 (95% CI: 0.0092–0.0223). In conclusion, manual segmentation of retinal image captured by smartphone-assisted fundus camera has good reliability (0.75 < ICC < 0.9) for Df analysis to study the morphology of retinal vasculatures.

Vessel Tree Extraction and Depth Estimation with OCT Images

2016 ◽  
pp. 23-33 ◽  
Author(s):  
Joaquim de Moura ◽  
Jorge Novo ◽  
Marcos Ortega ◽  
Noelia Barreira ◽  
Manuel G. Penedo
Keyword(s):  
Depth Estimation ◽  
Vessel Tree ◽  
Tree Extraction
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