Binder M, Kittler H, Seeber A, Steiner A, Pehamberger H, Wolff K
Department of Dermatology, University of Vienna Medical School, Austria.
Melanoma Res. 1998 Jun;8(3):261-6. doi: 10.1097/00008390-199806000-00009.
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 and 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.
落射荧光显微镜检查（ELM）是一种用于体内检查的非侵入性技术，可为色素沉着性皮肤病变（PSL）的临床诊断提供额外标准。在本研究中，我们试图确定PSL是否可以通过集成计算机系统进行自动诊断。该系统应识别PSL，自动提取特征，并利用这些特征训练人工神经网络，经过充分训练后，该网络应能够在无需人工辅助的情况下识别和分类新的PSL。本研究使用了120张随机选择的经组织学证实的PSL图像（33例普通痣、48例发育异常痣和39例恶性黑色素瘤）。这些图像通过数字方式获取，PSL的形态特征在无人工协助的情况下通过电子方式提取。然后将数值数据分为学习和测试案例，并与人工神经网络相连，用于训练以及对系统未训练过的病变进行进一步分类。我们的结果表明，计算机系统能够自动识别所呈现的95%的PSL。计算机系统的敏感性和特异性分别为90%和74%。相比之下，在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%，对发育异常痣为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极佳的来源；（2）通过相对简单（因而并非“智能”）的数字图像分析应用能够正确识别绝大多数PSL；（3）主要基于ABCD规则的自动特征提取为区分良性和恶性PSL提供了可靠数据；（4）有证据表明可以训练人工神经网络以充分区分良性和恶性PSL。
