Schindewolf T, Stolz W, Albert R, Abmayr W, Harms H
Institute of Virology and Immunology, University of Würzburg, Germany.
Anal Quant Cytol Histol. 1993 Feb;15(1):1-11.
The incidence of malignant melanoma, the most dangerous skin cancer, has increased rapidly during the last decade, and the figures are still rising. Since well-trained and experienced dermatologists are able to reach only a diagnostic accuracy of about 75% in visual preoperative classification, the discriminating ability of digital image analysis was evaluated in more than 350 malignant melanoma and benign melanocytic lesions that had all been confirmed histologically. Color slides of melanocytic lesions were scanned and digitized. Computer algorithms were programmed in FORTRAN on a DECstation 5000/200. A feature set was calculated describing the texture, color and their distributions as well as asymmetry, size and border of each lesion. These features, together with the histologic diagnosis, were the input in a commercial statistical classification program. In contrast to the accuracy of 75% achievable by the human eye, a correct classification rate of about 92% was reached with the mathematical classifier as compared with the histologic diagnosis.
恶性黑色素瘤是最危险的皮肤癌，在过去十年中其发病率迅速上升，且仍在增长。由于训练有素、经验丰富的皮肤科医生在术前视觉分类中只能达到约75%的诊断准确率，因此对数字图像分析的鉴别能力进行了评估，研究对象为350多个经组织学确诊的恶性黑色素瘤和良性黑素细胞病变。对黑素细胞病变的彩色幻灯片进行扫描并数字化。计算机算法用FORTRAN语言在DECstation 5000/200上编程。计算出一组描述每个病变的纹理、颜色及其分布以及不对称性、大小和边界的特征。这些特征与组织学诊断一起，作为商业统计分类程序的输入。与肉眼可达到的75%的准确率相比，数学分类器与组织学诊断相比达到了约92%的正确分类率。
