A Quantitative Analysis of Histogram Equalization-Based Methods on Fundus Images for Diabetic Retinopathy Detection

Suma, K. G.; Kumar, V. Saravana

doi:10.1007/978-981-13-0544-3_5

“…One of the ways to reduce noise in retinal diabetic retinopathy images is to use a Contrast Limited Adaptive Histogram Equalization (CLAHE) (16)(17)(18)(19). The CLAHE is applied to a small area of the image called a tile, not the whole image.…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

Zare,

Ramezanzadeh,

Shoeibi

et al. 2024

Preprint

0

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This study aims to develop an advanced automatic method to enhance the accuracy of blood vessel segmentation in Fundus Fluorescein angiography FFA images of patients with real Diabetic retinopathy (DR) during both early and late phases. A prospective randomized study of 280 eyes of 87 DR patients at various stages was included in this study. our approach involved using four different image enhancement techniques (Histogram equalization (HE), CLAHE, RMSHE, Proposed method (CLAHE+RMSHE)) and for each enhancement except HE, four thresholding methods including the proposed method (Modified Active Contour(MAC)+ IsoData), C-mean Fuzzy, Isodata, and MAC + otsu thresholding that are commonly used in eye fundus images across different datasets. The effectiveness of segmentation was evaluated using three metrics: Dice (early: 0.84±0.05, late:0.84±0.03), Jaccard (early:0.73±0.06, late:0.74±0.05), and BF score (early:0.98±0.02, late:0.97±0.02). We found that the RMSHE method was more effective in the late phase, while the combined CLAHE+RMSHE method worked better in the early phase. The best segmentation results for both early and late phases were achieved using a blood vessel segmentation approach based on the MAC + IsoData thresholding.

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“…5. (16)(17)(18)(19). The CLAHE is applied to a small area of the image called a tile, not the whole image.…”

Section: Recursive Mean-separate Histogram Equalization (Rmshe)mentioning

confidence: 99%

A High-Accuracy Segmentation Hybrid Method For Retinal Blood Vessel Detection In Fluorescein Angiography Images Of Real Diabetic Retinopathy Patients

Ramezanzadeh,

Shoeibi,

Shariati

et al. 2024

Preprint

0

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This study aims to develop an advanced automatic method to enhance the accuracy of blood vessel segmentation in Fundus Fluorescein angiography FFA images of patients with real Diabetic retinopathy (DR) during both early and late phases. A prospective randomized study of 280 eyes of 87 DR patients at various stages was included in this study. our approach involved using four different image enhancement techniques (Histogram equalization (HE), CLAHE, RMSHE, Proposed method (CLAHE+RMSHE)) and for each enhancement except HE, four thresholding methods including the proposed method (Modified Active Contour(MAC)+ IsoData), C-mean Fuzzy, Isodata, and MAC + otsu thresholding that are commonly used in eye fundus images across different datasets. The effectiveness of segmentation was evaluated using three metrics: Dice (early: 0.84±0.05, late:0.84±0.03), Jaccard (early:0.73±0.06, late:0.74±0.05), and BF score (early:0.98±0.02, late:0.97±0.02). We found that the RMSHE method was more effective in the late phase, while the combined CLAHE+RMSHE method worked better in the early phase. The best segmentation results for both early and late phases were achieved using a blood vessel segmentation approach based on the MAC + IsoData thresholding.

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“…Mouzai et al [12] presented a fuzzy-based gamma correction mechanism for brightness preservation. According to the study of [13], it is found that histogram-based techniques, especially histogram equalization (HE) and contrast limited adaptive histogram equalization, are mainly used to increase contrast medical images. However, contrast limited adaptive histogram equalization (CLAHE) is introduced as an improvised version of HE that uses a clip limiting mechanism, which reduces the over brightness and provides better enhancement results [14].…”

Section: Related Workmentioning

confidence: 99%

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

Krishnegowda¹,

Ganesan²

2022

IJECE

1

0

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<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.</p>

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A Quantitative Analysis of Histogram Equalization-Based Methods on Fundus Images for Diabetic Retinopathy Detection

Cited by 13 publications

References 10 publications

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

A High-Accuracy Segmentation Hybrid Method For Retinal Blood Vessel Detection In Fluorescein Angiography Images Of Real Diabetic Retinopathy Patients

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

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