Performance Evaluation of Joint Filtering and Histogram Equalization Techniques for Retinal Fundus Image Enhancement

2018 ◽  
Author(s):  
Shailesh Kumar ◽  
Shubham Choudhary ◽  
Rajeev Gupta ◽  
Basant Kumar
Keyword(s):  
Performance Evaluation ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Peishan Dai ◽  
Hanwei Sheng ◽  
Jianmei Zhang ◽  
Ling Li ◽  
Jing Wu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Median Filter ◽  
Basic Information ◽  
Fundus Image ◽  
Denoising Method ◽  
Convolution Algorithm ◽  
Retinal Fundus Image ◽  
Normalized Convolution ◽  
Retinal Fundus

Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.

scholarly journals Diabetic Retinopathy Detection using Image Processing

2019 ◽  
Vol 8 (6S) ◽  
pp. 937-941
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Histogram Equalization ◽  
Fundus Image ◽  
Adaptive Histogram Equalization ◽  
Retinal Fundus Image ◽  
Equalization Algorithm ◽  
Processing Techniques ◽  
Retinal Fundus

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

Performance of Image Enhancement Methods for Diabetic Retinopathy based on Retinal Fundus Image

2020 ◽  
Author(s):  
Nurul Atikah Binti Mohd Sharif ◽  
Nor Hazlyna Binti Harun ◽  
Yuhanis Binti Yusof ◽  
Zunaina Embong ◽  
Juhaida Binti Abu Bakar ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Image Enhancement ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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