Assessment of healing kinetics through true color image processing

Herbin, Michel; Bon, F.; Venot, Alain; Jeanlouis, F.; Dubertret, M.L.; Dubertret, Louis; Strauch, G.

doi:10.1109/42.222664

Cited by 77 publications

(49 citation statements)

References 8 publications

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“…Some combine features in more than one color space for better results, e.g. HIS and RGB (22)(23)(24)(25)31). Intensity characteristics (21) and ratios of maximum to minimum intensity value (29) have also been used.…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

“…The still camera is of use in some installations (24,25), while infra-red or ultraviolet illumination (in situ or in vivo) using the appropriate cameras is a popular Table I. Computer-based systems for the characterization of digital skin images.…”

Section: Systems In Literaturementioning

confidence: 99%

“…It is also interesting to include studies in our survey that discuss with the general problem of skin lesion image characterization, as they face similar problems. The lesions include tumor, crust, hair, scale, shiny and ulcer (15,19), erythema (20), burn scars (21) and wounds (24,25), among others.…”

Section: Systems In Literaturementioning

confidence: 99%

“…v) Skin lesion kinetics. Efforts have been made to elucidate the kinetics of skin lesions (24,25). The ratio of variances (RV) has been defined as (26): SD B 2 (standard deviation between days) is between day variance of the color variable computed using the mean values at each day of all wound sites and subjects.…”

Section: Definition Of Features For Detection Of Malignant Melanomamentioning

confidence: 99%

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Computational vision systems for the detection of malignant melanoma

Kosmopoulos¹

2006

Oncol Rep

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Abstract. In recent years, computational vision-based diagnostic systems for dermatology have demonstrated significant progress. We review these systems by first presenting the installation, visual features utilized for skin lesion classification and the methods for defining them. We also describe how to extract these features through digital image processing methods, i.e. segmentation, registration, border detection, color and texture processing, and present how to use the extracted features for skin lesion classification by employing artificial intelligence methods, i.e. discriminant analysis, neural networks, and support vector machines. Finally, we compare these techniques in discriminating malignant melanoma tumors versus dysplastic naevi lesions.

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Section: ------------------------------------------------------------mentioning

confidence: 99%

Section: Systems In Literaturementioning

confidence: 99%

Section: Systems In Literaturementioning

confidence: 99%

Section: Definition Of Features For Detection Of Malignant Melanomamentioning

confidence: 99%

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Computational vision systems for the detection of malignant melanoma

Kosmopoulos¹

2006

Oncol Rep

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“…(M. Herbin, 1993) ¦ measured as an angle between 0 and 360°(0°, 120°, 240° for the basic red R, (5) and saturation S measuring the relative white content of a color as having 4.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Crop Disease Leaf Image Segmentation Method Based on Color Features

Wang

Yang

Tian

Computer and Computing Technologies in Agriculture, Volume I

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Abstract:The color feature has been taken an important role in color image segmentation, especially in the fields of automatic detection of crop disease based on leaf image. In this paper an effective method for image segmentation of cucumber leaf images is proposed. First, the color space model is analyzed. Then a kind of color feature is applied to obtain the feature map, which combines RGB model and HSI model. Finally, the morphological method is used to accomplish the image segmentation. This method has been shown effective through experiments.crop disease, color image segmentation, feature extraction, color space

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Development of an image‐based measurement system for human facial skin colour

Xiao

Pointer

et al. 2021

Color Research & Application

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In this study, an image‐based measurement system was developed for human facial skin colour, involving the development of a digital imaging system, collection of facial skin colour from 60 human subjects, generation of different colour characterization models, and performance evaluation. The factors that affect facial skin colour characterization, including different training datasets (two colour charts and the collected facial skin colour dataset), mathematical mapping methods (linear transformation, polynomial regression, root‐polynomial regression and neural network) and camera image formats (JPG and RAW), were investigated and quantified not only by the conventional method of CIELAB colour difference, but also two newly introduced measures, facial colour contrast and skin colour gamut. The results indicate that the RAW image format for camera digital signals gave a more stable performance than the JPG format images, and the higher order polynomial regression with good predictive accuracy in terms of CIELAB colour difference did not perform well for the whole facial image. It is suggested to evaluate the model performance using the colour of both specific facial positions and the overall facial skin colour. Our comparative analysis in this study provides useful guidance for determining the colour characterization model for facial skin.

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Assessment of healing kinetics through true color image processing

Cited by 77 publications

References 8 publications

Computational vision systems for the detection of malignant melanoma

Computational vision systems for the detection of malignant melanoma

Crop Disease Leaf Image Segmentation Method Based on Color Features

Development of an image‐based measurement system for human facial skin colour

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