Enhance Contrast and Balance Color of Retinal Image

Dissopa, Jessada; Kansomkeat, Supaporn; Intajag, S.

doi:10.3390/sym13112089

Cited by 12 publications

(4 citation statements)

References 24 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the second filter to be used in our proposal. Which increases the brightness overall in the image [38]. It is simply lightness without any light.…”

Section: Ae Filtermentioning

confidence: 99%

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

El-Hoseny,

Elsepae,

Mohamed

et al. 2023

CMC

View full text Add to dashboard Cite

Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study's objective was to differentiate between different classes of diabetes and verify the model's capability to distinguish between these classes. The robustness of the model was evaluated using other metrics such as accuracy (ACC), precision (PRE), recall (REC), and area under the curve (AUC). In this particular study, the researchers utilized data cleansing techniques, transfer learning (TL), and convolutional neural network (CNN) methods to effectively identify and categorize the various diseases associated with diabetic retinopathy (DR). They employed the VGG-16CNN model, incorporating intelligent parameters that enhanced its robustness. The outcomes surpassed the results obtained by the auto enhancement (AE) filter, which had an ACC of over 98%. The manuscript provides visual aids such as graphs, tables, and techniques and frameworks to enhance understanding. This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR. The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

show abstract

“…This is the second filter to be used in our proposal. Which increases the brightness overall in the image [38]. It is simply lightness without any light.…”

Section: Ae Filtermentioning

confidence: 99%

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

El-Hoseny,

Elsepae,

Mohamed

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…The performance is evaluated on all the images of MESSIDOR and DRIVE datasets resulting in mean values of 4.60 Entropy, 23.78 PSNR, and 8.78 contrast-to-noise ratios. Dissopa et al 19 enhanced local image contrast by applying CLAHE on Lab space. Followed by histogram rescaling and stretching to standardize the brightness range to Hubbard’s brightness range of fundus images for different histogram clip limits.…”

Section: Related Workmentioning

confidence: 99%

“… Qureshi et al 18 MESSIDOR, DRIVE Non-linear contrast enhancement on J component of CIECAM02 color space. PSNR, contrast-to-noise ratio, intensity variation, Entropy Dissopa et al 19 DiaretDB0, STARE Local image contrast enhancement followed by standardization to Hubbard’s brightness range. Quaternion structural similarity, Global contrast factor, and lightness order error Kumar et al 20 STARE Brightness adjustment on value channel of HSV color space followed by weighted average histogram equalization.…”

Section: Related Workmentioning

confidence: 99%

Retinal image enhancement based on color dominance of image

Priyadharsini

2023

Sci Rep

View full text Add to dashboard Cite

Real-time fundus images captured to detect multiple diseases are prone to different quality issues like illumination, noise, etc., resulting in less visibility of anomalies. So, enhancing the retinal fundus images is essential for a better prediction rate of eye diseases. In this paper, we propose Lab color space-based enhancement techniques for retinal image enhancement. Existing research works does not consider the relation between color spaces of the fundus image in selecting a specific channel to perform retinal image enhancement. Our unique contribution to this research work is utilizing the color dominance of an image in quantifying the distribution of information in the blue channel and performing enhancement in Lab space followed by a series of steps to optimize overall brightness and contrast. The test set of the Retinal Fundus Multi-disease Image Dataset is used to evaluate the performance of the proposed enhancement technique in identifying the presence or absence of retinal abnormality. The proposed technique achieved an accuracy of 89.53 percent.

show abstract

“…Zhou et al performed retinal image enhancement based on luminosity enhancement using gamma correction in HSV color space followed by contrast enhancement in Lab color space [7]. Dissopa et al refined image contrast by applying CLAHE on Lab space and adjusted brightness by applying histogram equalization [15]. Most of the existing retinal enhancement methods are focussed towards improving the contrast by choosing green channel or luminosity channel.…”

Section: Introductionmentioning

confidence: 99%

Retinal image enhancement based on color dominance of image

Priyadharsini

Raju

2023

Preprint

View full text Add to dashboard Cite

Objective – Real-time fundus images captured to detect multiple diseases are prone to different quality issues like illumination, noise, etc., resulting in less visibility of anomalies. So, enhancing the retinal fundus images is essential for a better prediction rate of eye diseases. Method - In this paper, we propose Lab color space-based enhancement techniques for retinal image enhancement. Existing research works don’t consider the relation between color spaces of the fundus image in selecting a specific channel to perform retinal image enhancement. Our unique contribution to this research work is utilizing the color dominance of an image in quantifying the distribution of information in the blue channel and performing enhancement in Lab space followed by a series of steps to optimize overall brightness and contrast. Results - The test set of the Retinal Fundus Multi-disease Image Dataset is used to evaluate the performance of the proposed enhancement technique in identifying the presence or absence of retinal abnormality. The proposed technique achieved an accuracy of 89.53 percent.

show abstract

Enhance Contrast and Balance Color of Retinal Image

Cited by 12 publications

References 24 publications

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

Retinal image enhancement based on color dominance of image

Retinal image enhancement based on color dominance of image

Contact Info

Product

Resources

About