Classification of non-tumorous skin pigmentation disorders using voting based probabilistic linear discriminant analysis
Classification of non-tumorous skin pigmentation disorders using voting based probabilistic linear discriminant analysis
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DOI：
10.1016/j.compbiomed.2018.05.026
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发表时间：
2018-08-01
影响因子：
7.7
通讯作者：
Lin, Zhiping
中科院分区：
文献类型：
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作者：
Liang, Yunfeng;Sun, Lei;Lin, Zhiping
非肿瘤性皮肤色素沉着障碍会对患者产生巨大的负面情绪影响。正确诊断这些疾病对于制定适当的治疗方法至关重要。在本文中，我们提出了一种基于概率线性判别分析（PLDA）的计算机方法来分类五种非肿瘤性皮肤色素沉着障碍（即雀斑，小痣，Hori痣，黄褐斑和太田痣）。为了解决色素沉着图像类内方差大的问题，提出了一种基于投票的PLDA （V-PLDA）方法。提出的V-PLDA方法在包含150张取自患者的真实图像的数据集上进行了测试。结果表明，与原始的PLDA方法以及几种最先进的图像分类方法相比，本文提出的V-PLDA方法的分类准确率显著提高（方差分析（ANOVA）检验中p < 0.001，准确率达到4%或更高）。据作者所知，这是第一个关注非肿瘤性皮肤色素沉着图像分类问题的研究。因此，本文可以为该课题的后续研究提供一个标杆。此外，所提出的V-PLDA方法在与皮肤色素沉着障碍相关的临床应用中表现出良好的性能。
Non-tumorous skin pigmentation disorders can have a huge negative emotional impact on patients. The correct diagnosis of these disorders is essential for proper treatments to be instituted. In this paper, we present a computerized method for classifying five non-tumorous skin pigmentation disorders (i.e., freckles, lentigines, Hori's nevus, melasma and nevus of Ota) based on probabilistic linear discriminant analysis (PLDA). To address the large within-class variance problem with pigmentation images, a voting based PLDA (V-PLDA) approach is proposed. The proposed V-PLDA method is tested on a dataset that contains 150 real-world images taken from patients. It is shown that the proposed V-PLDA method obtains significantly higher classification accuracy (4% or more with p < 0.001 in the analysis of variance (ANOVA) test) than the original PLDA method, as well as several state-of-the-art image classification methods. To the authors' best knowledge, this is the first study that focuses on the non-tumorous skin pigmentation image classification problem. Therefore, this paper could provide a benchmark for subsequent research on this topic. Additionally, the proposed V-PLDA method demonstrates promising performance in clinical applications related to skin pigmentation disorders.