Color retinal image enhancement by Rayleigh contrast-limited adaptive histogram equalization

Jintasuttisak, Thani; Intajag, S.

doi:10.1109/iccas.2014.6987868

Cited by 38 publications

(27 citation statements)

References 7 publications

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“…As the input of the pretrained ResNet-18 model is 224 × 224, we adjust the kernel size of the GAP layer from 7 × 7 to 14 × 14. Following [8], we enhance fundus images by contrastlimited adaptive histogram equalization. Meanwhile, median filtering is applied on OCT images for noise reduction.…”

Section: Network Trainingmentioning

confidence: 99%

Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Wang

et al. 2019

Lecture Notes in Computer Science

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This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we introduce Loose Pair training, where a fundus image and an OCT image are paired based on class labels rather than eyes. Moreover, for a visual interpretation of how the individual modalities make contributions, we extend the class activation mapping technique to the multi-modal scenario. Experiments on a real-world dataset collected from an outpatient clinic justify the viability of our proposal for multi-modal AMD categorization.

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Section: Network Trainingmentioning

confidence: 99%

Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Wang

et al. 2019

Lecture Notes in Computer Science

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“…CLAHE was originally applied for enhancement of low-contrast medical images [23,24]. CLAHE differs from ordinary AHE in its contrast limiting.…”

Section: Clahe Algorithmmentioning

confidence: 99%

“…The redistributed histogram is different with ordinary histogram, because each pixel intensity is limited to a selected maximum. But the enhanced image and the original image have the same minimum and maximum gray values [24,28]. The CLAHE method to enhance the original image consists of the following steps:…”

Section: Clahe Algorithmmentioning

confidence: 99%

“…In order to overcome this problem, contrast limited adaptive histogram equalization (CLAHE) was proposed .CLAHE is a well-known block-based processing, and it can overcome the over amplification of noise problem in the homogeneous region of image with standard histogram equalization. CLAHE algorithm differs from standard HE in the respect that CLAHE operates on small regions in the image, called tiles, and computes several histograms, each corresponding to a distinct section of the image and use them to redistribute the lightness values of the image [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Fan

Yang

et al. 2017

Preprint

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Abstract:In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

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“…Hani [20] proposed an improved nonlinear hue-saturation-intensity color model (INHSI) to preserve color information of the retinal images. The intensity component is enhanced by Rayleigh transformation in contrast-limited adaptive histogram equalization (Rayleigh CLAHE) [21,23] algorithm.…”

Section: A Preprocessingmentioning

confidence: 99%

Diabetic retinopathy through retinal image analysis: A review

Reddy¹,

Ravindran²,

Kishore³

2017

IJET

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In this paper, the recent advancement in the Digital Image Processing Aspects in the Diabetic Retinopathy (DR) were been discussed. The major approaches in DR are categorized into four classes namely Preprocessing, Optic Disk Detection, Blood Vessel Extraction and supervised classification. The optic disk, blood vessels and exudates gives more analytical details about the retinal image, segmentation of those features are very important. Further these approaches are classified into finer classes based on the methodologies accomplished in the respective schemes. The details of the database those used for testing the proposed algorithms is also illustrated in this paper. The details of performance metrics such as accuracy, sensitivity, specificity, precision, recall and F-measure are also discussed through their mathematical expressions.

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Color retinal image enhancement by Rayleigh contrast-limited adaptive histogram equalization

Cited by 38 publications

References 7 publications

Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Diabetic retinopathy through retinal image analysis: A review

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