Universal colour quantisation for different colour spaces

Yu, Chia-Fu; Chen, S.-Y.

doi:10.1049/ip-vis:20050231

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…For I and S, the minimum and maximum values are 0 and 1, respectively. Mathematical details concerning RGB to HSI conversion are detailed elsewhere [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Mahmoud‐Ghoneim

2011

Theor Biol Med Model

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Texture analysis (TA) of histological images has recently received attention as an automated method of characterizing liver fibrosis. The colored staining methods used to identify different tissue components reveal various patterns that contribute in different ways to the digital texture of the image. A histological digital image can be represented with various color spaces. The approximation processes of pixel values that are carried out while converting between different color spaces can affect image texture and subsequently could influence the performance of TA. Conventional TA is carried out on grey scale images, which are a luminance approximation to the original RGB (Red, Green, and Blue) space. Currently, grey scale is considered sufficient for characterization of fibrosis but this may not be the case for sophisticated assessment of fibrosis or when resolution conditions vary. This paper investigates the accuracy of TA results on three color spaces, conventional grey scale, RGB, and Hue-Saturation-Intensity (HSI), at different resolutions. The results demonstrate that RGB is the most accurate in texture classification of liver images, producing better results, most notably at low resolution. Furthermore, the green channel, which is dominated by collagen fiber deposition, appears to provide most of the features for characterizing fibrosis images. The HSI space demonstrated a high percentage error for the majority of texture methods at all resolutions, suggesting that this space is insufficient for fibrosis characterization. The grey scale space produced good results at high resolution; however, errors increased as resolution decreased.

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“…For I and S, the minimum and maximum values are 0 and 1, respectively. Mathematical details concerning RGB to HSI conversion are detailed elsewhere [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Mahmoud‐Ghoneim

2011

Theor Biol Med Model

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“…Phenomenal colors also form part of this first category incorporating color spaces such as HSV (hue-saturation-value) and HSI, which are simply transformations from RGB space [30]. The HSV space is more akin to the human conceptual understanding of color [32]. The second category deals with application-based color space.…”

Section: Color Spacesmentioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Fan

Yang

et al. 2017

Preprint

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Abstract:In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

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“…Many color quantization researches [25][26][27][28][29] have been proposed to¯nd a more accurate and faster way for image compression or image segmentation. In this paper, the color quantization algorithm was for the¯rst time applied for urine color identi¯cation.…”

Section: Introductionmentioning

confidence: 99%

Urine Color Automatic Identification Device Based on Microcontroller Framework and Color Quantization Algorithm

Chuang

et al. 2014

Biomed. Eng. Appl. Basis Commun.

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Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among patients receiving treatment in medical institutions. Such infections can be detected from urine color, like in the case of the purple urine bag syndrome. However, it is a di±cult task for non-nursing care sta® and even the nursing sta® to correctly conduct naked-eye identi¯cation without proper tools. To better assist both nursing and non-nursing care sta® with the detection of infection signs in urine bag patients, a urine color automatic identi¯cation device has been developed. The device is based on microcontroller framework and color quantization algorithm. A hybrid color quantization algorithm and two features were proposed to identify the urine color. The identi¯ed color, as query data instead of human-described color keyword, can be used to retrieve the information from the database and then¯nd possible symptoms for early warning. Instead of the nursing sta®, the device can automatically identify the patient's urine color. From experimental results, the device with the proposed algorithm shows its capability and feasibility of the urine color automatic identi¯cation.

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Universal colour quantisation for different colour spaces

Cited by 8 publications

References 25 publications

Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Urine Color Automatic Identification Device Based on Microcontroller Framework and Color Quantization Algorithm

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