Extraction of acne lesion in acne patients from multispectral images

Fujii, H.; Yanagisawa, Takashi; Mitsui, Masanori; Murakami, Yuri; Yamaguchi, Masahiro; Ohyama, Nagaaki; Abe, Tokiya; Yokoi, Isao; Matsuoka, Yoshie; Kubota, Yasuo

doi:10.1109/iembs.2008.4650105

Cited by 37 publications

(19 citation statements)

References 5 publications

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“…The acne can be classified into several skin lesions including comedo, reddish papule, papule, pustule, and scar that are difficult for the proper evolution of acne skin lesion and that are infected in orthopaedic implants associated with the acne vulgaris [14]. P.acne is a gram positive anaerobic microorganism which the major skin bacterium causes the acne.…”

Section: Basic Knowledge Of Pacnementioning

confidence: 99%

Counting number of points for acne vulgaris using UV Fluorescence and image processing

Khongsuwan

Kiattisin

Wongseree

et al. 2012

The 4th 2011 Biomedical Engineering International Conference

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This paper presents counting number of points for the P.acne vulgaris using UV Fluorescence and image processing. This proposed method uses a process of image processing as follows. Cropping a UV image is to select a region of interest and, then the cropped image is resized for a suitable size and it (or the color image) is converted to a gray image. Quality of this gray image will be improved for image enhancement using adaptive histogram equalization. Finally, extended maxima transform, i.e. the regional maxima of the H-maxima transform, is used for counting number of the P.acne points. The experimental results show that the accuracy, sensitivity and precision are approximately at 83.75%, 98.22% and 85.04%, respectively. It saves time to analysis of the acne points.

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Section: Basic Knowledge Of Pacnementioning

confidence: 99%

Counting number of points for acne vulgaris using UV Fluorescence and image processing

Khongsuwan

Kiattisin

Wongseree

et al. 2012

The 4th 2011 Biomedical Engineering International Conference

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“…The advancements of technology today can aid health workers and volunteers in addressing this problem by developing Computer-assisted diagnosis systems that can guide in diagnosing disease by utilizing pattern recognition schemes to identify skin disease present in an image. There are several systems [1] [2] [3] [6] that use image pre-processing and machine learning as the main means to identify skin lesions present in an image. These systems use the extracted features from images as training input to their machine learning models using different machine learning algorithms such as the Artificial Neural Network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of a Multi-model, Multi-level Artificial Neural Network for Eczema Skin Lesion Detection

Guzman

Maglaque

Torres

et al. 2015

2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)

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There are several current systems developed to identify common skin lesions such as eczema that utilize image processing and most of these apply feature extraction techniques and machine learning algorithms. These systems extract the features from pre-processed images and use them for identifying the skin lesions through machine learning as the core. This paper presents the design and evaluation of a system that implements a multi-model, multi-level system using the Artificial Neural Network (ANN) architecture for eczema detection. In this work, multi-model system is defined as architecture with different models depending on the input characteristic. The outputs of these models are integrated by a decision layer, thus multi-level, which computes the probability of an eczema case. The resulting system has 68.37% average confidence level as opposed to the 63.01% of the single level, i.e. single model, system in the actual testing of eczema versus non-eczema cases. Furthermore, the multi-model, multi-level design produces more stable models in the training phase wherein overfitting was reduced.

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“…Also, it takes a lot of time and effort [1], [2], [3]. Thus, there is an evolution on creating computer-aided detection program in recent years [4]. Regular way to detect acnes is to use shape detection.…”

Section: Introductionmentioning

confidence: 99%

“…But there are limitations which is it still requires manual inspection. [9] Humaynn utilizes a template matching approach used for locating the correlated object and it is applied for localizing the acne lesion. This approach proved to be affective for counting and recognizing of lesions by letting a system learn the appearance of acne in advance to do matching.…”

Section: Introductionmentioning

confidence: 99%

Automatic acne detection for medical treatment

Chantharaphaichi¹,

Uyyanonvara²,

Sinthanayothin

et al. 2015

2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

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In this paper, an effective image processing technique has been studied to develop a system of acne detection. The focus is on binary thresholding applied to facial images with various types, shapes or amounts of acne. A typical image on a cheek has been usually used to take in the experiment which results are markings on acnes automatically, this method is more effective than manual counting by a typical dermatologist. An input image is first taken into gray and special color model from regular red-green-blue version, this work is for comparison and revelation of region of interest. The regions of interest are mostly acnes but it can contain some noise. Then binary threshold has been used to clearly show and can remove the noise easier from regions of acne. A box shape is constructed to mark the final result of acne detection, so that correct and incorrect detected acne can be displayed and calculated for a system efficiency. Experimental output can be summarized that accuracy, precision and sensitivity are quite fluctuated depending on color, shape and lighting condition of acnes in the image.

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Extraction of acne lesion in acne patients from multispectral images

Cited by 37 publications

References 5 publications

Counting number of points for acne vulgaris using UV Fluorescence and image processing

Counting number of points for acne vulgaris using UV Fluorescence and image processing

Design and Evaluation of a Multi-model, Multi-level Artificial Neural Network for Eczema Skin Lesion Detection

Automatic acne detection for medical treatment

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