Low-contrast X-ray enhancement using a fuzzy gamma reasoning model

Mouzai, Meriem; Tarabet, Chahrazed; Mustapha, Aouache

doi:10.1007/s11517-020-02122-y

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…Xiao et al [11] adopted a gamma correction mechanism for the fundus image enhancement for the automatic detection of DR extracting the green component of the fundus image and determined the histogram curve slope. 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.…”

Section: Related Workmentioning

confidence: 99%

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

Krishnegowda¹,

Ganesan²

2022

IJECE

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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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Section: Related Workmentioning

confidence: 99%

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

Krishnegowda¹,

Ganesan²

2022

IJECE

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“…The purpose of introducing a fuzzy enhancement operator [23] is to enhance the contrast of the image region by different enhancement processing, that is, the black target area is attenuated and the white background area is enhanced. The enhanced image not only retains the information of the original image but also makes the layers of each region clearer, which further reduces the image ambiguity.…”

Section: Data Preprocessing Based On Fuzzy Enhancementmentioning

confidence: 99%

A Novel Ray-Casting Algorithm Using Dynamic Adaptive Sampling

Wang

Pan

et al. 2020

Wireless Communications and Mobile Computing

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Ray-casting algorithm is an important volume rendering algorithm, which is widely used in medical image processing. Aiming to address the shortcomings of the current ray-casting algorithms in 3D reconstruction of medical images, such as slow rendering speed and low sampling efficiency, an improved algorithm based on dynamic adaptive sampling is proposed. By using the central difference gradient method, the corresponding sampling interval is obtained dynamically according to the different sampling points. Meanwhile, a new rendering operator is proposed based on the color value and opacity changes before and after the ray enters the volume element, and the resistance luminosity. Compared with the state of other algorithms, experimental results show that the method proposed in this paper has a faster rendering speed while ensuring the quality of the generated image.

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