scholarly journals Obtaining Consensus Annotations For Retinal Image Segmentation Using Random Forest And Graph Cuts

2015 ◽  
Author(s):  
Dwarikanath Mahapatra ◽  
Joachim M. Buhmann
Keyword(s):  
Image Segmentation ◽  
Random Forest ◽  
Retinal Image ◽  
Graph Cuts ◽  
Retinal Image Segmentation

A Study on Retinal Image Segmentation and Registration Methods

2021 ◽  
pp. 25-33
Author(s):  
B. Sivaranjani ◽  
C. Kalaiselvi
Keyword(s):  
Image Segmentation ◽  
Retinal Image ◽  
Template Matching ◽  
Diagnosis And Treatment ◽  
Retinal Images ◽  
Fundus Images ◽  
Image Preprocessing ◽  
Fundus Camera ◽  
Motion Parameters ◽  
Retinal Image Segmentation

Diagnosis and treatment of several disorders affecting the retina and the choroid behind it require capturing a sequence of fundus images using the fundus camera. These images are to be processed for better diagnosis and planning of treatment. Retinal image template matching is greatly required to extract certain features that may help in diagnosis and treatment. Also registration of retinal images is very useful in extracting the motion parameters that help in composing a complete map for the retina as well as in retinal tracking. This paper introduces a survey for the image preprocessing, dimensionality reduction, template matching and registration techniques that were reported as being well for retinal images.

scholarly journals Deep Retinal Image Segmentation With Regularization Under Geometric Priors

2020 ◽  
Vol 29 ◽  
pp. 2552-2567 ◽  
Author(s):  
Venkateswararao Cherukuri ◽  
Vijay Kumar B.G. ◽  
Raja Bala ◽  
Vishal Monga
Keyword(s):  
Image Segmentation ◽  
Retinal Image ◽  
Retinal Image Segmentation

Contrast normalization steps for increased sensitivity of a retinal image segmentation method

2017 ◽  
Vol 11 (8) ◽  
pp. 1509-1517 ◽  
Author(s):  
Toufique Ahmed Soomro ◽  
Mohammad A. U. Khan ◽  
Junbin Gao ◽  
Tariq M. Khan ◽  
Manoranjan Paul
Keyword(s):  
Image Segmentation ◽  
Retinal Image ◽  
Segmentation Method ◽  
Increased Sensitivity ◽  
Contrast Normalization ◽  
Retinal Image Segmentation

scholarly journals Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

2021 ◽  
Vol 17 (14) ◽  
pp. 103-118
Author(s):  
Mohammed Enamul Hoque ◽  
Kuryati Kipli
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Retinal Image ◽  
Feature Recognition ◽  
Image Features ◽  
Complex Data ◽  
Retinal Image Segmentation

Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

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