A Pixel-Based Skin Segmentation in Psoriasis Images Using Committee of Machine Learning Classifiers

Y. George, M. Aldeen, R. Garnavi
{"title":"A Pixel-Based Skin Segmentation in Psoriasis Images Using Committee of Machine Learning Classifiers","authors":"Y. George, M. Aldeen, R. Garnavi","doi":"10.1109/DICTA.2017.8227398","DOIUrl":null,"url":null,"abstract":"Skin segmentation, which involves detecting human skin areas in an image, is an important process for skin disease analysis. The aim of this paper is to identify the skin regions in a newly collected set of psoriasis images. For this purpose, we present a committee of machine learning (ML) classifiers. A psoriasis training set is first collected by using pixel values in five different color spaces. Experiments are then performed to investigate the impact of both the size of the training set and the number of features per pixel, on the performance of each skin classifier. A committee of classifiers is constructed by combining the classification results obtained from seven distinct skin classifiers using majority voting. Also, we propose a refinement method using morphological operations to improve the resulted skin map. We use a set of 100 psoriasis images for training and testing. For comparative evaluation, we consider 3283 face skin images. Finally, F-measure and accuracy are used to evaluate the performance of the classifiers. The experimental results show that the size of the training set does not greatly influence the overall performance. The results also indicate that the feature vector using pixel values in the five color spaces has higher performance than any subset of these spaces. Comparative study suggests that the proposed method performs reasonably with both psoriasis and faces skin images, with accuracy of 97.4% and 80.41% respectively.","PeriodicalId":194175,"journal":{"name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA.2017.8227398","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

Skin segmentation, which involves detecting human skin areas in an image, is an important process for skin disease analysis. The aim of this paper is to identify the skin regions in a newly collected set of psoriasis images. For this purpose, we present a committee of machine learning (ML) classifiers. A psoriasis training set is first collected by using pixel values in five different color spaces. Experiments are then performed to investigate the impact of both the size of the training set and the number of features per pixel, on the performance of each skin classifier. A committee of classifiers is constructed by combining the classification results obtained from seven distinct skin classifiers using majority voting. Also, we propose a refinement method using morphological operations to improve the resulted skin map. We use a set of 100 psoriasis images for training and testing. For comparative evaluation, we consider 3283 face skin images. Finally, F-measure and accuracy are used to evaluate the performance of the classifiers. The experimental results show that the size of the training set does not greatly influence the overall performance. The results also indicate that the feature vector using pixel values in the five color spaces has higher performance than any subset of these spaces. Comparative study suggests that the proposed method performs reasonably with both psoriasis and faces skin images, with accuracy of 97.4% and 80.41% respectively.
基于像素的银屑病图像的机器学习分类器分割
皮肤分割是皮肤病分析的一个重要步骤,它涉及到在图像中检测人体皮肤区域。本文的目的是在一组新收集的牛皮癣图像中识别皮肤区域。为此,我们提出了一个机器学习(ML)分类器委员会。首先利用5个不同颜色空间的像素值收集银屑病训练集。然后进行实验来研究训练集的大小和每像素的特征数量对每个皮肤分类器性能的影响。采用多数投票法,将七个不同皮肤分类器的分类结果结合起来,组成一个分类器委员会。此外,我们还提出了一种使用形态学操作来改进结果皮肤图的改进方法。我们使用一组100张牛皮癣图像进行训练和测试。为了进行比较评估,我们考虑了3283张面部皮肤图像。最后,使用F-measure和准确率来评估分类器的性能。实验结果表明,训练集的大小对整体性能影响不大。结果还表明,在五个颜色空间中使用像素值的特征向量比这些空间的任何子集具有更高的性能。对比研究表明,该方法对牛皮癣和面部皮肤图像均有较好的识别效果,准确率分别为97.4%和80.41%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信