Application of the real-time Retinex image enhancement for endoscopic images

Okuhata, Hiroyuki; Nakamura, Hajime; Hara, Shinsuke; Tsutsui, Hiroshi; Onoye, Takao

doi:10.1109/embc.2013.6610273

Cited by 33 publications

(22 citation statements)

References 7 publications

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“…It is interesting to note that based on a single objective quality metric it difficult to assess the diagnosis values of these images in relation to the subjective evaluation of these images by medical doctors. Many of other related works in capsule image enhancement use standard natural image quality metrics [5][6][7][8]. However, these metrics are proposed for natural images where relative smoothness is preferred.…”

Section: Objective Evaluation and Comparisonmentioning

confidence: 99%

“…By applying frequency domain transformation to the input image using DFT and appropriate design of Butterworth filter they claimed to obtain an enhancement result compared to contrast limited adaptive histogram equalization (CLAHE) [5]. More recently, Okuhata et al [6] applied retinex theory to the problem of CVE image enhancement. The authors modelled the problem with a total variational model algorithm that was constructed to minimize the cost function in terms of reflectance and illuminance images.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Capsule Endoscopy Image Enhancement

Mohammed¹,

Farup²,

Pedersen³

et al. 2018

Preprint

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Abstract:Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detail layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared to state of the art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

show abstract

Section: Objective Evaluation and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Capsule Endoscopy Image Enhancement

Mohammed¹,

Farup²,

Pedersen³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…It is interesting to note that based on a single objective quality metric it difficult to assess the diagnosis values of these images in relation to the subjective evaluation of these images by medical doctors. Many of other related works in capsule image enhancement use standard natural image quality metrics [5,6,8,9]. However, these metrics are proposed for natural images where relative smoothness is preferred.…”

Section: Objective Evaluation and Comparisonmentioning

confidence: 99%

“…By applying frequency domain transformation to the input image using DFT and appropriate design of Butterworth filter they claimed to obtain an enhancement result compared to contrast limited adaptive histogram equalization (CLAHE) [5]. More recently, Okuhata et al [6] applied retinex theory to the problem of CVE image enhancement [7]. The authors modelled the problem with a total variational model algorithm that was constructed to minimize the cost function in terms of reflectance and illuminance images.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Capsule Endoscopy Image Enhancement

et al. 2018

View full text Add to dashboard Cite

Abstract:Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detailed layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared it to state-of-the-art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

show abstract

“…Color enhancement technique at the chip level or as a post-processing step is another method to increase the image quality and diagnostic yield [14]. The Fuji Intelligent Color Enhancement (FICE, Fujinon Inc.) system [15], narrow-band imaging (NBI) [16], I-scan [17], and retinex [18] are the examples of post-processing color enhancement algorithms which have been widely used [6,19].…”

Section: Introductionmentioning

confidence: 99%

Non-uniform illumination endoscopic imaging enhancement via anti-degraded model and L 1 L 2-based variational retinex

Rao

Luo

et al. 2017

J Wireless Com Network

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In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L 1 L 2 -based variational retinex (AD-L 1 L 2 VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between the detail component of the haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using the proposed L 1 L 2 -based variational retinex (L 1 L 2 VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods.

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Application of the real-time Retinex image enhancement for endoscopic images

Cited by 33 publications

References 7 publications

Stochastic Capsule Endoscopy Image Enhancement

Stochastic Capsule Endoscopy Image Enhancement

Stochastic Capsule Endoscopy Image Enhancement

Non-uniform illumination endoscopic imaging enhancement via anti-degraded model and L 1 L 2-based variational retinex

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