Digital processing of endoscopic color images

Ohyama, Nagaaki; Suzuki, Keisuke; Honda, Toshio; Tsujiuchi, Jumpei; Ono, Rentaro; Ikeda, Susumu

doi:10.1016/0030-4018(85)90336-0

Cited by 20 publications

(9 citation statements)

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“…The unsharp masking algorithm is applied to the S and L components of the entire image [3]. In addition, Ohyama et al [4] described a strategy for color emphasis by linearly magnifying the value of S in the LHS color space.…”

Section: The Luminance-hue-saturation Color Spacementioning

confidence: 99%

“…Color emphasis [4] can be simply implemented in the YNINQ color space by magnifying the value of NS3. However, there is a problem for finding a proper magnification factor with linear magnification of the saturation component.…”

Section: Color Image Enhancement In a Nonlinear Color Difference Bmentioning

confidence: 99%

“…Therefore, the histogram equalization of the saturation value in the LHS color space has been introduced to enhance color images [2] [3]. In [4], Ohyama et al described a strategy for color emphasis in the LHS color space. However, the nonlinearity of the transformations between the RGB and LHS coordinates is so complicated that the developed color image enhancement algorithms can not be efficiently implemented.…”

Section: Introductionmentioning

confidence: 99%

“…With the 3-D model, a perceptual color can be defined in terms of its luminance, hue and saturation, and interaction between colors can cause a change in all these parameters. To directly describe luminance, hue and saturation of color, the luminance-hue-saturation (LHS) color space has been introduced [2] [3] [4].…”

Section: Introductionmentioning

confidence: 99%

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<title>Color image enhancement in a new color space</title>

1996

SPIE Proceedings

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A practical approach to developing color image enhancement algorithms is to transform the R, G and B components of each pixel into another set of color coordinates with which luminance, hue and saturation of color can be described. Each of the new set of coordinates is processed with its own enhancement algorithm, and the enhanced new set of coordinates are inverse transformed to R, G and B components for display. In this paper, we introduce a new color space based on a concept of nonlinear color differences. The transformations between the proposed color space and the RGB color space is much simpler than that between the LHS and the RGB color spaces. Meanwhile, the chromatic components are totally uncorrelated with the achromatic component, with which hue and saturation of color can be exactly described. With the proposed color space obtained by modifying the YIQ color space, we develop some color image processing algorithms for luminance component equalization, saturation equalization, and color emphasis.

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Section: The Luminance-hue-saturation Color Spacementioning

confidence: 99%

Section: Color Image Enhancement In a Nonlinear Color Difference Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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<title>Color image enhancement in a new color space</title>

1996

SPIE Proceedings

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“…It is typically employed to pre-process images to improve the accuracy of the quantitative image analyses [16][17][18][19][20]. Multispectral enhancements are implemented to either improve our visual perception of the objects in a multispectral image or to particularly detect an object which is not visually perceptible [2][3][4][5][6][7][8][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images

Bautista

Yagi

2012

Analytical Cellular Pathology

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Abstract. In this paper we proposed a multispectral enhancement scheme in which the spectral colors of the stained tissuestructure of interest and its background can be independently modified by the user to further improve their visualization and color discrimination. The colors of the background objects are modified by transforming their N-band spectra through an NxN transformation matrix, which is derived by mapping the representative samples of their original spectra to the spectra of their target colors using least mean square method. On the other hand, the color of the tissue structure of interest is modified by modulating the transformed spectra with the sum of the pixel's spectral residual-errors at specific bands weighted through an NxN weighting matrix; the spectral error is derived by taking the difference between the pixel's original spectrum and its reconstructed spectrum using the first M dominant principal component vectors in principal component analysis. Promising results were obtained on the visualization of the collagen fiber and the non-collagen tissue structures, e.g., nuclei, cytoplasm and red blood cells (RBC), in a hematoxylin and eosin (H&E) stained image.

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Statistical spectrum discrimination for endoscopic imaging

Kikuchi¹,

Ohyama²

1995

Bioimaging

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We design spectral functions for endoscopic color filters by applying a statistical classification method to discriminate between human gastric spectra of normal and bloody tissues. Estimated RGB color images are generated from light intensities taken through the color discriminant filters. We propose a visualization method for future electronic endoscopes which can display conventional color images with effective enhancement of bloody areas.

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Digital processing of endoscopic color images

Cited by 20 publications

References 1 publication

<title>Color image enhancement in a new color space</title>

<title>Color image enhancement in a new color space</title>

Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images

Statistical spectrum discrimination for endoscopic imaging

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