Independent-component analysis of skin color image

Tsumura, Norimichi; Haneishi, Hideaki; Miyake, Yoichi

doi:10.1364/josaa.16.002169

Cited by 192 publications

(115 citation statements)

References 9 publications

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“…3) The observed color signals and the quantities of them keep the linearity in the optical density domain. Tsumura et al [9] extracted components of hemoglobin and melanin from a human skin color image by using an ICA algorithm. Based on the linearity assumption, the skin color model in the optical density domain of RGB channels can be expressed by the color den- are relative quantities of two pigments.…”

Section: Skin Decompositionmentioning

confidence: 99%

Detection of Skin Pigmentation using Independent Component Analysis

Liu¹,

Lee²,

Kwon³

et al. 2013

Journal of Korea Multimedia Society

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This paper presents an approach for detecting and measuring human skin pigmentation. In the proposed scheme, we extract a skin area by a Gaussian skin color model that is estimated from the statistical analysis of training images and remove tiny noises through the morphology processing. A skin area is decomposed into two components of hemoglobin and melanin by an independent component analysis (ICA) algorithm. Then, we calculate the intensities of hemoglobin and melanin by using the location histogram and determine the existence of skin pigmentation according to the global and local distribution of two intensities. Furthermore, we measure the area and density of the detected skin pigmentation. Experimental results verified that our scheme can both detect the skin pigmentation and measure the quantity of that and also our scheme takes less time because of the location histogram.

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Section: Skin Decompositionmentioning

confidence: 99%

Detection of Skin Pigmentation using Independent Component Analysis

Liu¹,

Lee²,

Kwon³

et al. 2013

Journal of Korea Multimedia Society

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“…Tsumura et al first suggested using a simple Lambert-Beer type of law for radiance from a multilayer skin surface, resulting from illumination by polarized light [5]. That is, employing a model similar to a simple logarithm model based on optical densities for accounting for light passing for example through multilayer slide film.…”

Section: Colour Space Image Formulationmentioning

confidence: 99%

Intrinsic Melanin and Hemoglobin Colour Components for Skin Lesion Malignancy Detection

Madooei

Drew

Sadeghi

et al. 2012

Lecture Notes in Computer Science

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Abstract. In this paper we propose a new log-chromaticity 2-D colour space, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: (i) the effects of the particular camera characteristics for the camera system used in forming RGB images; (ii) the colour of the light used in the dermoscope; (iii) shading induced by imaging non-flat skin surfaces; (iv) and light intensity, removing the effect of light-intensity falloff toward the edges of the dermoscopic image. In the context of a blind source separation of the underlying colour, we arrive at intrinsic melanin and hemoglobin images, whose properties are then used in supervised learning to achieve excellent malignant vs. benign skin lesion classification. In addition, we propose using the geometric-mean of colour for skin lesion segmentation based on simple greylevel thresholding, with results outperforming the state of the art.

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“…Tsumura et al (1999) applied the independent component analysis method to the pixel values of color images of faces and also extracted two independent color components corresponding to melanin and hemoglobin. These are indeed located in two different layers of the skin.…”

Section: State Of the Artmentioning

confidence: 99%

Assessing human skin color from uncalibrated images

Marguier

Bhatti

Baker

et al. 2007

Int J Imaging Syst Tech

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Images of a scene captured with multiple cameras will have different color values because of variations in color rendering across devices. We present a method to accurately retrieve color information from uncalibrated images taken under uncontrolled lighting conditions with an unknown device and no access to raw data, but with a limited number of reference colors in the scene. The method is used to assess skin tones. A subject is imaged with a calibration target. The target is extracted and its color values are used to compute a color correction transform that is applied to the entire image. We establish that the best mapping is done using a target consisting of skin colored patches representing the whole range of human skin colors. We show that color information extracted from images is well correlated with color data derived from spectral measurements of skin. We also show that skin color can be consistently measured across cameras with different color rendering and resolutions ranging from 0.1 to 4.0 megapixels.

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Independent-component analysis of skin color image

Cited by 192 publications

References 9 publications

Detection of Skin Pigmentation using Independent Component Analysis

Detection of Skin Pigmentation using Independent Component Analysis

Intrinsic Melanin and Hemoglobin Colour Components for Skin Lesion Malignancy Detection

Assessing human skin color from uncalibrated images

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