Color medical image enhancement based on adaptive equalization of intensity numbers matrix histogram

Gu, Juping; Hua, Liu; Wu, Xia; Yang, Hui; Zhou, Zhentao

doi:10.1007/s11633-014-0871-9

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…The CII has been widely used to measure contrast enhancement. [36][37][38][39] CII computes the increase in contrast in the output image respect to the input image. It is given by the ratio of the contrast index of the output image and the input image (see expression (8)).…”

Section: CIImentioning

confidence: 99%

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Intriago-Pazmiño

Ibarra-Fiallo

Crespo³

et al. 2020

Health Informatics J

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Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.

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Section: CIImentioning

confidence: 99%

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Intriago-Pazmiño

Ibarra-Fiallo

Crespo³

et al. 2020

Health Informatics J

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“…Digital images are created using several different methods ranging from mobile phone cameras for daily usage to satellite cameras for aerial images and from simple scanners for documents to medical scanners for the human body, and several more [3,4]. These different methods and conditions can lead to several undesirable effects, such as blurring, and degraded contrast and brightness as well as an incorrect balance of color levels [3][4][5][6]. Therefore, image enhancement techniques have attracted the attention of many researchers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, image enhancement techniques have attracted the attention of many researchers. Furthermore, image enhancement techniques aim to improve the quality of the visual appearance of images that suffer from the previously mentioned problems [1][2][3][4][5][7][8][9]. This can make the processed image appear clearer and more useful than the original image for its purpose [3,4,8,10].…”

Section: Introductionmentioning

confidence: 99%

A Global Two-Stage Histogram Equalization Method for Gray-Level Images

Almotairi

2020

J. ICT Res. Appl.

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Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of wellknown methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods.

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“…presented a digital image processing for the color of flame. For color image processing, Lissner, Gu, Kim et al [4][5][6]. implemented a unified color space based on perceptual image processing .…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Color Images

Han

2016

Electronics, Communications and Networks V

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The extraction of certain characteristics points such as color edge, inflection points, etc., is an imaging problem which requires urgent attention. This paper proposes a similar color segment algorithm. The algorithm is analyzed in different color distribution situations, and the extraction effect to the color is shown. Additionally, experimental analysis of the algorithms is provided. Experimental results indicate that the similar color segment algorithm performs better than existing algorithms in relation to a more obvious color edge, as it has better edge detection, stronger anti-noise ability, a faster processing speed and other advantages. Moreover, this paper compares the proposed algorithm to existing classical feature extraction algorithms.

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Color medical image enhancement based on adaptive equalization of intensity numbers matrix histogram

Cited by 14 publications

References 9 publications

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

A Global Two-Stage Histogram Equalization Method for Gray-Level Images

Feature Extraction for Color Images

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