Medical images enhancement by using anisotropic filter and CLAHE

Kurt, Burçin; Nabiyev, Vasif V.; Turhan, Kemal

doi:10.1109/inista.2012.6246971

Cited by 34 publications

(14 citation statements)

References 5 publications

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“…Outra técnica utilizada foi o CLAHE. Este método de realceé amplamente utilizado em problemas oftálmicos para a detecção automática de micro aneurismas ou segmentação de vasos sanguíneos da retina [Kurt et al 2012]. O CLAHE opera através da equalização adaptativa do histograma que fornece um aumento de contraste baseado em pequenas partições da imagem.…”

Section: Aprimoramento De Contrasteunclassified

Detecção de Patologias Oculares em Imagens de Reﬂexo Vermelho Utilizando Descritores de Cor

Martins

Santos

Almeida

et al. 2021

Anais Do XXI Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2021)

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O Teste de Brückner é um exame oftalmológico que visa detectar patologias oculares precocemente. Ele opera através da identiﬁcação de um reﬂexo vermelho na região ocular após a incidência de um feixe luminoso. Este trabalho apresenta um novo método para identiﬁcação automática de um possível problema ocular em imagens de reﬂexo vermelho oriundas do Teste de Brückner através de descritores de cor. Utilizando uma otimização na escolha dos métodos de pré processamento em conjunto com os descritores de cor dominante e os momentos de cor, o método proposto alcançou 92% de acurácia, 98% de especiﬁcidade e 76% de sensibilidade utilizando o classiﬁcador XGBoost.

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Section: Aprimoramento De Contrasteunclassified

Detecção de Patologias Oculares em Imagens de Reﬂexo Vermelho Utilizando Descritores de Cor

Martins

Santos

Almeida

et al. 2021

Anais Do XXI Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2021)

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show abstract

“…Here, four color spaces, RGB, HSV, YCrCb, and Lab (or CIELAB), were deployed through their channels. The features f 0 and f 1 were calculated on the output of an image sent to a CLAHE (contrast limited adaptative histogram equalization) filter [29]. In that case, the CLAHE filter was applied only on the V (brightness) channel of the HSV space.…”

Section: Cloud Segmentationmentioning

confidence: 99%

Global Horizontal Irradiance Modeling for All Sky Conditions Using an Image-Pixel Approach

Campos

Tiba

2020

Energies

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Ground images with a sky camera have become common to evaluate cloud coverage, aerosols, and energy collection. In parallel, the growth of solar energy has led to an impulse to evaluate and forecast the solar potential in a site before investments, which has increased the importance of solar power measurements. Facing that scenario, this work presents a novel sky camera model that allows to measure the global horizontal irradiance (GHI). Initially, images from a fisheye camera were stored and a pixel-based approach model was created for cloud segmentation. A total of 813 k vectors of features were used as input to the support vector machine for classification (SVC), which yielded a success rate of about 98.6% in accuracy. The Sun’s position was also segmented and an artificial neural network (ANN) regression model for GHI with 17 input features was created based on segmentation of the Sun, clouds, and sky. The training/validation stage of the ANN used 89,964 samples and the test stage reached about 97.4% in Pearson’s correlation. The RMSE was 72.3 W/m2 for GHI and the normalized RMSE, nRMSE, revealed 12.9% for GHI. That nRMSE value was comparable to or lower than other studies, despite the high fluctuations in the observed GHI.

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“…The work carried out by Hua et al [22] have used Young-Helmholtz based transformation scheme in order to enhance colored radiological images. Kurt et al [23] have used adaptive histogram as well as anisotrophic filters in order to perform enhancement of medical images. Xiurong [24] have implemented a masking algorithm of adaptive nature in order to effectively control the sharpening of the medical images.…”

Section: Introductionmentioning

confidence: 99%

Framework for comprehensive enhancement of brain tumor images with single-window operation

Murthy¹,

Sadashivappa²

2020

IJECE

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Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.

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Medical images enhancement by using anisotropic filter and CLAHE

Cited by 34 publications

References 5 publications

Detecção de Patologias Oculares em Imagens de Reﬂexo Vermelho Utilizando Descritores de Cor

Detecção de Patologias Oculares em Imagens de Reﬂexo Vermelho Utilizando Descritores de Cor

Global Horizontal Irradiance Modeling for All Sky Conditions Using an Image-Pixel Approach

Framework for comprehensive enhancement of brain tumor images with single-window operation

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