Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study
Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study
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DOI：
10.1067/mjd.2001.110395
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发表时间：
2001-02-01
影响因子：
13.8
通讯作者：
Wang, S
中科院分区：
文献类型：
--
作者：
Elbaum, M;Kopf, AW;Wang, S
背景：黑色素瘤和黑素细胞痣的区分即使对皮肤癌专家来说也是困难的。这激发了人们对计算机辅助病变图像分析的兴趣。目的：我们的目的是通过多光谱数字皮肤镜提供黑色素瘤与发育不良和其他黑色素细胞痣的全自动鉴别。方法：在4个临床中心，对活检前怀疑为黑色素瘤的色素病变进行成像，获取每个病变的10张灰度（MelaFind）图像，每张图像在可见光和近红外光谱的不同部分。通过计算机专家系统对63例黑色素瘤（33例浸润性，30例原位）和183例黑色素细胞痣（其中111例发育不良）的图像进行自动处理，将黑色素瘤与痣分离。专家系统使用线性或非线性分类器。培训和测试这些分类器的“金标准”是由两位皮肤病理学家进行一致诊断。结果：在再替代上，w参数线性分类器的灵敏度为100%，特异性为85%，12参数非线性分类器的灵敏度为100% /73%。在留一交叉验证下，线性分类器给出100%/84%（灵敏度/特异性），而非线性分类器给出95%/68%。红外图像特征显著，基于小波分析的特征也显著。结论：通过多光谱数字皮肤镜自动鉴别浸润性和原位黑色素瘤与黑色素细胞痣是可行的。
Background: Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images.Objective: Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy.Method: At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The "gold standard" for training and testing these classifiers was concordant diagnosis by two dermatopathologists.Results: On resubstitution, 100% sensitivity was achieved at 85% specificity with a W-parameter linear classifier and 100%-/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis.Conclusion: Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.