• ETRI Journal
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• v.28 no.6
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• pp.770-776
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• 2006

This paper presents a hyperspectral imaging technique based on laser-induced fluorescence for non-invasive detection of tumorous tissue on mouse skin. Hyperspectral imaging sensors collect image data in a number of narrow, adjacent spectral bands. Such high-resolution measurement of spectral information reveals contiguous emission spectra at each image pixel useful for the characterization of constituent materials. The hyperspectral image data used in this study are fluorescence images of mouse skin consisting of 21 spectral bands in the visible spectrum of the wavelengths ranging from 440 nm to 640 nm. Fluorescence signal is measured with the use of laser excitation at 337 nm. An acousto-optic tunable filter (AOTF) is used to capture images at 10 nm intervals. All spectral band images are spatially registered with the reference band image at 490 nm to obtain exact pixel correspondences by compensating the spatial offsets caused by the refraction differences in AOTF at different wavelengths during the image capture procedure. The unique fluorescence spectral signatures demonstrate a good separation to differentiate malignant tumors from normal tissues for rapid detection of skin cancers without biopsy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.80-96
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• 2022

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1048-1052
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• 2004

In this work, we have investigated a role of linking parameter in Pulse-Coupled Neural Network(PCNN) which is suggested to explain the synchronous activities among neurons in the cat cortex. Then we have found a method to determine the linking parameter for a satisfactory face detection performance in a given color image. Face detection algorithm which uses the color information is independent on pose, size and obstruction of a face. But the use of color information encounters some problems arising from skin-tone color in the background, intensity variation within faces, and presence of random noise and so on. Depending on these conditions, PCNN's linking parameters should be selected an appropriate values. First we obtained the mean and variance of the skin-tone colors by experiments. Then, we introduced a preprocess that the pixel with a mean value of skin-tone colors has the highest level value (255) and the other pixels have values between 0 and 255 according to normal distribution with a variance. This preprocessing leads to an easy decision of the linking parameter of the Pulse-Coupled Neural Network. Through experiments, it is verified that the proposed method can improve the face detection performance compared to the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1456-1464
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• 2007

The research of skin-tone detection has been conducting continuously to enlarge the importance in security, surveillance and administration of the information and 'Password Control System' for using face and skin recognition in airports, harbors and general companies. As well as tile rapid diffusion of the application range in image communications and an electron transaction using wide range of communication network, the importance of the accurate detection of skin color has been augmenting recently. In this paper, it will set up the boundaries of skin colors using the information of Cb and Cr in YCbCr color model of human skin color which is from hundreds compiled portrait images for each race, and suggest a efficient yet simple structure about the skin detection which has been followed by whether the comprehension of the boundaries of skin or not with adaptive skin-range set. With the possibility of the 1D Processes which does not use any memory, it is able to be applied to relatively small-sized hardware and system such as mobile apparatuses. To add the selective mode, it is not only available the improvement of tie skin detection, but also showing the correspondent results about previous face recognition technologies using complicated algorithm.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.113-121
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• 2005

Face detection in real-time video constitutes one of the major trend in face recognition. In this paper, we propose a face detection algorithm using the skin color and Haar-like feature in real-time video. The proposed algorithm is followed by three sequences; First, moving objects are detected by difference-method in YCbCr coordinates, and then by using Haar-like features, face candidate regions of the moving objects is selected. Finally we extract the most possible face candidates by comparing the pixel values of face candidates with the skin color. In order to prevent a mistake. we use similar features or skin color to detect a face by selecting a adaptive ROI and improve the processing speed in real-time video. The computer simulation shows the validity of the proposed method that the processing speed is improved by 30% than previous works and the detection success rate is 96.8%.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1397-1403
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• 2018

Today, modern people perceive skin care as part of their physical health care, and acne is a common skin disease problem that is found on the face. In this paper, an effective acne detection algorithm using CIE $L^*a^*b^*$ color space has been proposed. It is red when the pixel value of the component image $a^*$ is a positive number, so it is suitable for detecting acne in skin image. First, the skin image based on the RGB color space is subjected to light compensation through color balancing, and converted into a CIE $L^*a^*b^*$ color space. The extracted component image $a^*$ was normalized, and then the skin and acne area were estimated with the threshold values. Experimental results show that the proposed method detects acne more effectively than the conventional method based on brightness information, and the proposed method is robust against the reflected light source.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5257-5264
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• 2016

Background: Though people of color (POC) are less likely to become afflicted with skin cancer, they are much more likely to die from it due to delay in detection or presentation. Very often, skin cancer is diagnosed at a more advanced stage in POC, making treatment difficult.The purpose of this research was to improve awareness regarding skin cancers in people of color by providing recommendations to clinicians and the general public for early detection and photo protection preventive measures. Methods: Data on different types of skin cancers were presented to POC. Due to limited research, there are few resources providing insights for evaluating darkly pigmented lesions in POC. Diagnostic features for different types of skin cancers were recorded and various possible risk factors were considered. Results: This study provided directions for the prevention and early detection of skin cancer in POC based on a comprehensive review of available data. Conclusions: The increased morbidity and mortality rate associated with skin cancer in POC is due to lack of awareness, diagnosis at a more advanced stage and socioeconomic barriers hindering access to care. Raising public health concerns for skin cancer prevention strategies for all people, regardless of ethnic background and socioeconomic status, is the key to timely diagnosis and treatment.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.971-972
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• 2008

In this paper, we present a novel method for classifying pornography images using adaptive skin detection. From an input image, we detect initial skin regions and construct an adaptive skin probability density model using color information for the detected skin regions. From the skin probability density model, we extract feature vectors and train the images using Support Vector Machine to classify pornography images.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.