Klaus H, Roth K, Hufnagl P, Wildner G P
Arch Geschwulstforsch. 1985;55(4):253-8.
50 fine-needle biopsies of mammary tumors (20 medullary carcinomas, 20 fibroadenomas, and 10 false negative judged cases) were studied by automated microscopic image analysis. Eight morphometric and densitometric features of the tumor cell nuclei were determined on Papanicolaou stained smears. It was analyzed whether smears false judged as negative can be rightly allocated as carcinomas by the automated method. The results of this study have shown that it was possible by means of three cell nucleus parameters (standard deviation of the area, mean area ratio, and skewness of the grey value gradient at contours) with 100% correct separation of the unequivocal cytologies into malignant and benign smears to recognize about 90% of the false judged smears rightly as carcinomas. The results are reproducible and can be obtained in a time of 2-5 min per smear. The first results show that the automated microscope image analysis may be a valuable tool in the diagnosis of borderline-cases of mammary tumors.
采用自动显微镜图像分析技术对50例乳腺肿瘤细针穿刺活检标本（20例髓样癌、20例纤维腺瘤和10例假阴性判断病例）进行了研究。在巴氏染色涂片上测定了肿瘤细胞核的8个形态计量学和密度测定特征。分析了被错误判断为阴性的涂片是否能通过自动方法正确地归类为癌。本研究结果表明，借助三个细胞核参数（面积标准差、平均面积比和轮廓灰度值梯度的偏度），可以将明确的细胞学涂片100%正确地分为恶性和良性涂片，从而正确识别约90%被错误判断的涂片为癌。结果具有可重复性，每张涂片可在2 - 5分钟内获得结果。初步结果表明，自动显微镜图像分析可能是诊断乳腺肿瘤临界病例的一种有价值的工具。
