Contrast enhancement in retinal image via multi-scale geometrical analysis

2006 ◽  
Vol 2 (3) ◽  
pp. 229-232 ◽  
Author(s):  
Peng Feng ◽  
Ying-jun Pan ◽  
Biao Wei ◽  
Wei Jin ◽  
De-ling Mi
Keyword(s):  
Contrast Enhancement ◽  
Retinal Image ◽  
Geometrical Analysis ◽  
Multi Scale

An Automatic Stopping Criterion for Contrast Enhancement Using Multi-scale Top-Hat Transformation

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Cesar Bustacara-Medina ◽  
Leonardo Flórez-Valencia
Keyword(s):  
Contrast Enhancement ◽  
Stopping Criterion ◽  
Multi Scale

Grayscale image contrast enhancement based on multi-scale edge representation

2005 ◽  
Author(s):  
Guangtao Zhai ◽  
Wenjun Zhang ◽  
Xiaokang Yang
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Grayscale Image ◽  
Multi Scale ◽  
Image Contrast Enhancement

Interactive Multi-scale Contrast Enhancement of Previously Processed Digital Mammograms

2010 ◽  
pp. 31-38
Author(s):  
Fabian Zöhrer ◽  
Markus T. Harz ◽  
Anke Bödicker ◽  
Heike Seyffarth ◽  
Kathy J. Schilling ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Multi Scale

23-2: An Adaptive Contrast Enhancement of Image Using Multi-Scale Histogram Representation

2017 ◽  
Vol 48 (1) ◽  
pp. 318-321 ◽  
Author(s):  
Yu-Feng Jin ◽  
Shen-Sian Syu ◽  
Ming-Jong Jou
Keyword(s):  
Contrast Enhancement ◽  
Multi Scale ◽  
Histogram Representation

Single image dehazing via multi-scale gradient domain contrast enhancement

2016 ◽  
Vol 10 (3) ◽  
pp. 206-214 ◽  
Author(s):  
Zetian Mi ◽  
Yijun Zheng ◽  
Huan Zhou ◽  
Minghui Wang
Keyword(s):  
Contrast Enhancement ◽  
Single Image ◽  
Image Dehazing ◽  
Multi Scale ◽  
Gradient Domain ◽  
Single Image Dehazing

Contrast Enhancement Based on Encoder-decoder Network Using Multi-Scale Feature Map

2020 ◽  
Vol 57 (5) ◽  
pp. 74-86
Author(s):  
Sanghyun Byun ◽  
Heunseung Lim ◽  
Soohwan Yu ◽  
Joonki Paik
Keyword(s):  
Contrast Enhancement ◽  
Feature Map ◽  
Scale Feature ◽  
Multi Scale

Adaptive multi-scale contrast enhancement through non-linear pyramid recombination

1990 ◽  
Vol 11 (11) ◽  
pp. 735-742 ◽  
Author(s):  
Alexander Toet
Keyword(s):  
Contrast Enhancement ◽  
Multi Scale ◽  
Non Linear

scholarly journals Multi-Scale and Multi-Branch Convolutional Neural Network for Retinal Image Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 365
Author(s):  
Yun Jiang ◽  
Wenhuan Liu ◽  
Chao Wu ◽  
Huixiao Yao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Retinal Image ◽  
Spatial Information ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Feature Information ◽  
The Impact ◽  
Retinal Image Segmentation

The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information extraction. They are susceptible to the impact of the lesion area and poor image quality, resulting in the poor recovery of contextual information. This also causes the segmentation results of the model to be noisy and low in accuracy. Therefore, this paper proposes a multi-scale and multi-branch convolutional neural network model (multi-scale and multi-branch network (MSMB-Net)) for retinal image segmentation. The model uses atrous convolution with different expansion rates and skip connections to reduce the loss of feature information. Receiving domains of different sizes captures global context information. The model fully integrates shallow and deep semantic information and retains rich spatial information. The network embeds an improved attention mechanism to obtain more detailed information, which can improve the accuracy of segmentation. Finally, the method of this paper was validated on the fundus vascular datasets, DRIVE, STARE and CHASE datasets, with accuracies/F1 of 0.9708/0.8320, 0.9753/0.8469 and 0.9767/0.8190, respectively. The effectiveness of the method in this paper was further validated on the optic disc visual cup DRISHTI-GS1 dataset with an accuracy/F1 of 0.9985/0.9770. Experimental results show that, compared with existing retinal image segmentation methods, our proposed method has good segmentation performance in all four benchmark tests.

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