Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network
Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network
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DOI：
10.1097/00008390-199806000-00009
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发表时间：
1998-06-01
影响因子：
2.2
通讯作者：
Wolff, K
中科院分区：
文献类型：
--
作者：
Binder, M;Kittler, H;Wolff, K
发光显微镜（Epiluminescence microscopy，ELM）是一种非侵入性的活体检测技术，可为色素性皮肤病变的临床诊断提供额外的标准。该系统应识别PSL，自动提取特征，并使用这些特征训练人工神经网络，如果训练充分，人工神经网络应能够在没有人工帮助的情况下识别和分类新的PSL。本研究中使用了120张随机选择的经组织学证实的PSL图像（33例常见痣，48例发育不良痣和39例恶性黑色素瘤）。数字化获得的图像和形态学特征的PSLs的电子提取没有人的帮助。数值数据，然后分为学习和测试的情况下，并连接到一个人工神经网络的训练和进一步分类的病变，该系统没有被训练on. Our结果表明，计算机化的系统能够自动识别95%的PSL。该系统的敏感性和特异性分别为90%和74%。相比之下，当区分不同类型的病变时，该系统对恶性黑色素瘤的真阳性率仅为38%，对发育不良痣为62%，对普通痣为33%。我们的数据表明：（1）PSL的ELM图像为数字图像分析提供了一个很好的来源;（2）绝大多数PSL可以通过相对简单的方法正确识别。（3）主要基于ABCD规则的自动特征提取提供了关于良性和恶性PSL之间的区分的可靠数据;和（4）有证据表明人工神经网络可以被训练以充分区分良性和恶性PSL，（C）1998 Lippincott-Raven Publishers。
Epiluminescence microscopy (ELM) is a non-invasive technique for in vivo examination which can provide additional criteria for the clinical diagnosis of pigmented skin lesions (PSLs), In the present study we attempt to determine whether PSLs can be automatically diagnosed by an integrated computerized system. This system should recognize the PSL, automatically extract features and use these features in training an artificial neural network, which should - if sufficiently trained - be capable of recognizing and classifying a new PSL without human aid. One hundred and twenty images of randomly selected histologically proven PSLs (33 common naevi, 48 dysplastic naevi and 39 malignant melanomas) were used in this study. The images were digitally obtained and the morphological features of the PSLs were extracted electronically without human assistance. The numerical data were then divided into learning and testing cases and linked to an artificial neural network for training and for further classification of lesions that the system had not been trained on. Our results show that the computerized system was able to automatically identify 95% of the PSLs presented. The sensitivity a:nd specificity of the computerized system were 90% and 74%, respectively. In contrast, when differentiating between individual types of lesions, the system performed at true positive rates of only 38% for malignant melanoma, 62% for dysplastic naevi and 33% for common naevi. Our data indicate that (1) ELM images of PSLs provide an excellent source for digital image analysis; (2) the vast majority of PSLs can be correctly identified by a relatively simple (and thus not 'intelligent') application of digital image analysis; (3) automatic feature extraction based mainly on ABCD rules provides reliable data on the distinction between benign and malignant PSLs; and (4) there is evidence that artificial neural networks can be trained to adequately discriminate between benign and malignant PSLs, (C) 1998 Lippincott-Raven Publishers.