Gerger Armin, Wiltgen Marco, Langsenlehner Uwe, Richtig Erika, Horn Michael, Weger Wolfgang, Ahlgrimm-Siess Verena, Hofmann-Wellenhof Rainer, Samonigg Hellmut, Smolle Josef
Department of Internal Medicine, Division of Oncology, Medical University Graz, Graz, Austria.
Skin Res Technol. 2008 Aug;14(3):359-63. doi: 10.1111/j.1600-0846.2008.00303.x.
BACKGROUND/PURPOSE: In this study we assessed the applicability of image analysis and a machine learning algorithm on diagnostic discrimination of benign and malignant melanocytic skin tumours in in vivo confocal laser-scanning microscopy (CLSM).
A total of 857 CLSM tumour images including 408 benign nevi and 449 melanoma images was evaluated. Image analysis was based on features of the wavelet transform. For classification purposes we used a classification tree software (CART). Moreover, automated image analysis results were compared with the prediction success of an independent human observer.
CART analysis of the whole set of CLSM tumour images correctly classified 97.55% and 96.32% of melanoma and nevi images. In contrast, sensitivity and specificity of 85.52% and 80.15% could be reached by the human observer. When the image set was randomly divided into a learning (67% of the images) and a test set (33% of the images), overall 97.31% and 81.03% of the tumour images in the learning and test set could be classified correctly by the CART procedure.
Provided automated decisions can be used as a second opinion. This can be valuable in assisting diagnostic decisions in this new and exciting imaging technique.
背景/目的：在本研究中，我们评估了图像分析和机器学习算法在体内共聚焦激光扫描显微镜（CLSM）中对良性和恶性黑素细胞性皮肤肿瘤进行诊断鉴别的适用性。
共评估了857张CLSM肿瘤图像，其中包括408例良性痣和449例黑色素瘤图像。图像分析基于小波变换的特征。为了进行分类，我们使用了分类树软件（CART）。此外，还将自动图像分析结果与独立人类观察者的预测成功率进行了比较。
对整套CLSM肿瘤图像进行CART分析，黑色素瘤和痣图像的正确分类率分别为97.55%和96.32%。相比之下，人类观察者的敏感性和特异性分别为85.52%和80.15%。当将图像集随机分为学习集（67%的图像）和测试集（33%的图像）时，CART程序对学习集和测试集中的肿瘤图像总体正确分类率分别为97.31%和81.03%。
假设自动诊断结果可作为参考意见。这对于辅助这种新型且令人兴奋的成像技术的诊断决策可能具有重要价值。
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