Automatic retinal image registration based on blood vessels feature point

2003 ◽  
Author(s):  
Er-Hu Zhang ◽  
Yan Zhang ◽  
Tian-Xu Zhang
Keyword(s):  
Image Registration ◽  
Blood Vessels ◽  
Retinal Image ◽  
Feature Point

Image registration based on feature point sets

2003 ◽  
Author(s):  
Quanbing Zhang ◽  
Bin Luo ◽  
Sui Wei ◽  
Shangjun Yang
Keyword(s):  
Image Registration ◽  
Feature Point ◽  
Point Sets

Gaussian field estimator with manifold regularization for retinal image registration

2019 ◽  
Vol 157 ◽  
pp. 225-235 ◽  
Author(s):  
Jiahao Wang ◽  
Jun Chen ◽  
Huihui Xu ◽  
Shuaibin Zhang ◽  
Xiaoguang Mei ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Manifold Regularization ◽  
Gaussian Field

scholarly journals Analysis and Research of Subclinical Diabetic Peripheral Vascular Lesions Based on Mutual Information-based Medical Image Registration Algorithm and Colour Ultrasound Imaging (Preprint)

2020 ◽  
Author(s):  
Nailong Jia ◽  
Long Fan ◽  
Chuanzi Li ◽  
Zhongshi Nie ◽  
Suihuang Wang ◽  
...  
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Blood Vessels ◽  
Optimization Algorithm ◽  
Peripheral Blood ◽  
Medical Image ◽  
Hybrid Optimization ◽  
Medical Image Registration ◽  
Hybrid Optimization Algorithm ◽  
Subclinical Diabetes

BACKGROUND Background: At present, the incidence of diabetes is on the rise. When doctors diagnose and treat patients' diseases, they often need to image patients to provide complementary information on patient anatomy and functional metabolism. OBJECTIVE Objective: The aim was to understand the morphological features of peripheral blood vessels of diabetes more accurately and explore its Risk factors for the occurrence of lesions for early diagnosis and early prevention. METHODS Methods: The paper selected subclinical diabetes patients admitted to our hospital from October 2013 to October 2018 as a research object. After performing colour Doppler ultrasonography on peripheral blood vessels, images of ultrasound images were taken. Then the paper proposes a multi-mode medical image registration method based on hybrid optimization algorithm for the multi-extreme problem of mutual information function. Mutual information is used as the similarity measure. The hybrid optimization algorithm is used to search for the best registration exchange parameters. The quasi-colour super images are exchanged for registration purposes. RESULTS Results: The experimental results show that the hybrid optimization algorithm can accurately analyse the colour ultrasound image of the peripheral blood vessels of subclinical diabetes, avoiding falling into the local optimal value, and the accuracy of the registration result reaches the sub-pixel level. CONCLUSIONS Conclusion: With the rapid development of imaging technology, the increasing image resolution, and the increasing amount of image data, parallel performance is high. The quasi-method has a very important significance for multi-modal medical image registration. The parameters in this algorithm can be further optimized. CLINICALTRIAL

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.

scholarly journals RETINAL IMAGE REGISTRATION AND COMPARISON FOR CLINICAL DECISION SUPPORTDi Xiao

2012 ◽  
Vol 05 (09) ◽  
Author(s):  
Di Xiao ◽  
Janardhan Vignarajan ◽  
Jane Lock ◽  
Shaun Frost ◽  
Mei-Ling Tay Kearney ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Clinical Decision
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