scholarly journals Enhance Contrast and Balance Color of Retinal Image

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2089
Author(s):  
Jessada Dissopa ◽  
Supaporn Kansomkeat ◽  
Sathit Intajag
Keyword(s):  
Retinal Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Age Related Macular Degeneration ◽  
Image Brightness ◽  
Symmetric Information ◽  
Age Related ◽  
Contrast Enhanced ◽  
Retinal Fundus Image ◽  
Equalization Technique

This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions.

Retinal Image Analysis: A Review

2012 ◽  
pp. 265-269
Author(s):  
S. R. Nirmala ◽  
Malaya Kumar Nath ◽  
Samarendra Dandapat
Keyword(s):  
Image Analysis ◽  
Retinal Image ◽  
Screening Tool ◽  
Review Paper ◽  
Age Related Macular Degeneration ◽  
Hypertensive Retinopathy ◽  
Human Errors ◽  
Remote Areas ◽  
Age Related ◽  
The Relationship

Images of the eye ground or retina not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automated retina image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This can in turn be used to reduce human errors or to provide services to remote areas. In this review paper, we discuss some of the current techniques used to automatically detect the important clinical features of retinal image, such as the blood vessels, optic disc and macula. The quantitative analysis and measurements of these features can be used to better understand the relationship between various diseases and the retinal features.

scholarly journals Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration

2021 ◽  
Vol 11 (11) ◽  
pp. 1127
Author(s):  
Arun Govindaiah ◽  
Abdul Baten ◽  
R. Theodore Smith ◽  
Siva Balasubramanian ◽  
Alauddin Bhuiyan
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Retinal Image ◽  
Prediction Models ◽  
Retinal Imaging ◽  
Image Data ◽  
Age Related Macular Degeneration ◽  
Learning Models ◽  
Age Related ◽  
Machine Learning Models

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.

Retinal-Image Quality and Contrast-Sensitivity Function in Age-Related Macular Degeneration

2010 ◽  
Vol 35 (8) ◽  
pp. 757-761 ◽  
Author(s):  
Carolina Ortiz ◽  
José R. Jiménez ◽  
Francisco Pérez-Ocón ◽  
José J Castro ◽  
Rosario González-Anera
Keyword(s):  
Image Quality ◽  
Macular Degeneration ◽  
Contrast Sensitivity ◽  
Retinal Image ◽  
Sensitivity Function ◽  
Age Related Macular Degeneration ◽  
Contrast Sensitivity Function ◽  
Age Related

scholarly journals Fuzzy Based Combinatorial Filter Model for Drusen Enhancement in Retinal Fundus Images

2019 ◽  
Vol 8 (2S3) ◽  
pp. 801-808
Keyword(s):  
Fuzzy Inference ◽  
Similarity Index ◽  
Age Related Macular Degeneration ◽  
Inference System ◽  
Filter Model ◽  
Age Related ◽  
Retinal Fundus Image ◽  
Feature Similarity Index ◽  
Bit Planes ◽  
Retinal Fundus

This article presents an approach to enhance drusen regions in retinal fundus image of a patient having Age-related Macular Degeneration (AMD). In this approach, a new filter model is developed by combining two processes on images which is named as Combinatorial Filter Model (CFM). The first process is based on processing drusen significant image bit planes and the second process is based on implementing fuzzy inference system (FIS) on bit planes. When bit planes are independently processed, the result improves the visibility of drusen features. An FIS is constructed to process the bit planes to further enhance the processed image. This approach is tested on images with drusen features on good quality images in proprietary database and comparatively low quality images from STARE database. The objective study of this model shows that drusen elements are enhanced and validated to a 95% significant level. The quality of the enhanced image is evaluated for preservation of drusen features using a proven feature similarity index technique with 0.99 quality index values

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