Retinal image enhancement using low-pass filtering and α-rooting

Cao, Lvchen; Li, Huiqi; Zhang, Yanjun

doi:10.1016/j.sigpro.2019.107445

Cited by 38 publications

(24 citation statements)

References 17 publications

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“…Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to the luminance channel of LAB colorspace is useful for retinal fundus enhancement [1]. We compare it to Algo.…”

Section: Comparison To Existing Workmentioning

confidence: 99%

Enhancement of Retinal Fundus Images via Pixel Color Amplification

Gaudio¹,

Smailagic²,

Campilho³

2020

Lecture Notes in Computer Science

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We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multitask segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together.

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“…Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to the luminance channel of LAB colorspace is useful for retinal fundus enhancement [1]. We compare it to Algo.…”

Section: Comparison To Existing Workmentioning

confidence: 99%

Enhancement of Retinal Fundus Images via Pixel Color Amplification

Gaudio¹,

Smailagic²,

Campilho³

2020

Lecture Notes in Computer Science

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“…Such low-quality images make it hard for the ophthalmologists to make a clear diagnosis or reduce the performance of automatic retina examination models. Thus, improvement of the visibility of the anatomical structure through image synthesis along with the acquisition of a variety of retinal image patterns have been required [16][17][18][19][20][21][22].…”

Section: Challenges For Synthesized Retinal Imagesmentioning

confidence: 99%

“…The result showed that they were useful in the diagnosis by the ophthalmologists or achieved a sufficient level of improvement to be used as a preprocessing step for the automated retinal analysis systems. Meanwhile, Cao improved the contrast in the retinal structure by using a low-pass filter (LPF) and the α-rooting in an attempt to make the images clearer, and at the same time, the gray scale [22] was used to restore colors. Additionally, the performance of the proposed method was compared with the four aforementioned methods [17][18][19]21] and the result statistically proved that the method was relatively superior in terms of visual and quantitative evaluations (i.e., contrast enhancement measurement, color difference, and overall quality).…”

Section: Challenges For Synthesized Retinal Imagesmentioning

confidence: 99%

Intelligent Image Synthesis for Accurate Retinal Diagnosis

2020

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Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.

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“…Recently, a retinal image enhancement was proposed via low-pass filtering and αrooting [12]. The images were improved in four steps: (1) background padding to prevent a boundary over enhancement, (2) contrast improvement by removing low frequency in the input image's root domain, (3) grayscale adjustment in all color channels to recover the original color, and (4) refinement process to enhance the result image's contrast.…”

Section: Introductionmentioning

confidence: 99%

Enhance Contrast and Balance Color of Retinal Image

2021

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This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions.

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Retinal image enhancement using low-pass filtering and α-rooting

Cited by 38 publications

References 17 publications

Enhancement of Retinal Fundus Images via Pixel Color Amplification

Enhancement of Retinal Fundus Images via Pixel Color Amplification

Intelligent Image Synthesis for Accurate Retinal Diagnosis

Enhance Contrast and Balance Color of Retinal Image

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