子宫癌自动细胞筛查的基础研究。I. 模式识别系统的特征评估。
Fundamental study of automatic cyto-screening for uterine cancer. I. Feature evaluation for the pattern recognition system.
作者信息
Tanaka N, Ikeda H, Ueno T, Takahashi M, Imasato Y
出版信息
Acta Cytol. 1977 Jan-Feb;21(1):72-8.
A basic study was carried out to determine the parameters of a pattern recognition system for the automatic assessment of cytologic cell samples. Various cell features were extracted, whose combinations were evaluationed by an "ambiguity function". It was shown that the highest reliability can be obtained with a combination of the features of nuclear staining, nuclear area, area of cytoplasm, nuclear/cytoplasmic ratio, nuclear shape and chromatin pattern. However, recognition of the nuclear edge and chromatin patterns is complicated and makes automation difficult. Even if these two features are omitted, false positives do not exceed 20 per cent. Consequently screening of abnormal cells can be carried out by image recognition procedures by the use of a computer.
开展了一项基础研究以确定用于自动评估细胞学细胞样本的模式识别系统的参数。提取了各种细胞特征，并通过“模糊函数”对其组合进行评估。结果表明，将核染色、核面积、细胞质面积、核/质比、核形状和染色质模式等特征组合起来可获得最高的可靠性。然而，核边缘和染色质模式的识别很复杂，使得自动化难以实现。即便省略这两个特征，假阳性率也不会超过20%。因此，可通过计算机利用图像识别程序来筛查异常细胞。
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