Fundus Image Enhancement Using EAL-CLAHE Technique

Fundus disorders like Glaucoma are a serious health risk that affects people all over the world, lowering their quality of life. Optic disc (OD) segmentation is a crucial step for automatic detection of glaucoma with fundus images. This paper presents an efficient and accurate OD segmentation methodology from eye fundus images. In the proposed methodology, fundus images are transformed from RGB (Red, Green, and Blue) color space to LAB (Luminance, A‐axis, B‐axis) color space. A new image is generated using the luminance, A‐axis component, and image information content. As a pre‐segmentation stage, the unsupervised approach extracts the region of interest containing OD. The adaptive threshold method, followed by morphological operations, extracts the contours of the OD region and removes the spur. Segmentation of OD is a challenging task due to similar intensity levels with neighborhood, vascular occlusion, and retinal atrophy. The proposed work overcomes the challenges by segmentation of the OD at three levels, which enhances the functionality at different illumination conditions. The features are extracted at various levels to determine whether the segmented image contains the OD or not. The efficiency of the proposed methodology demonstrates by applying it to a variety of fundus images from datasets, namely ISBI IDRiD, DRISHTI‐GS, and SYSU. Key performance metrics like Intersection over Union and Dice Coefficient are used to evaluate the performance of the proposed methodology. The proposed methodology achieves Intersection over Union of 0.996, 0.997, and 0.994 on ISBI IDRiD, DRISHTI‐GS, and SYSU datasets, respectively. Similarly, the proposed methodology obtains 0.96, 0.97, and 0.98 Dice Coefficient on the three datasets.

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“…The RGB OD mask is converted to gray scale OD mask. The CLAHE method 48 is used to enhance the mask image, and its complement (

BNE (italicODI)

) is computed. A morphological opening operation is performed on this image.…”

Section: Proposed Methodologymentioning

confidence: 99%

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

Prakash

Kakarla

2023

Int J Imaging Syst Tech

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“…After edge enhancement, the color and luminosity of the image are enhanced with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE). The CLAHE ( Chandni et al, 2022 ) creates a realistic form of the image by improvising the color pattern and luminosity by histogram equalization. The sample image obtained after enhancing the color and luminosity is represented in Figure 4 as follows.…”

Section: Lbacs-lstm Methodsmentioning

confidence: 99%

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Bhansali,

Patra,

Abouhawwash

et al. 2023

Front. Bioeng. Biotechnol.

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Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).

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“…Because blood vessels appear more contrast than the background in the green channels, Setiawan et al [60] split the color fundus image into three individual channels, and performed CLAHE on the green channel, and then merged these three channels to improve the color retinal image quality. Chandni et al [61] proposed a modified CLAHE using a Laplacian operator and HSV color model to obtain better information on the luminance of the color fundus image and preserve its edges. In addition to contrast enhancement, illumination correction is another way to enhance image contrast by reducing the illumination uniformity error of the retinal images.…”

Section: A Image Enhancementmentioning

confidence: 99%

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Zhu,

Xiong,

Zhong

et al. 2024

IEEE Access

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Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and blindness. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to further analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed.

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Fundus Image Enhancement Using EAL-CLAHE Technique

Cited by 3 publications

References 8 publications

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

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