Generative Adversarial Network and Retinal Image Segmentation

2020 ◽  
pp. 473-490
Author(s):  
Talha Iqbal ◽  
Hazrat Ali
Keyword(s):  
Image Segmentation ◽  
Retinal Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
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

scholarly journals An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

2020 ◽  
Vol 10 (15) ◽  
pp. 5032
Author(s):  
Xiaochang Wu ◽  
Xiaolin Tian
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Chest Radiographs ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Adversarial Network ◽  
Cardiac Segmentation ◽  
Adversarial Networks ◽  
Feature Extractor

Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.

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
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