Detweiler R, Zahniser D J, Garcia G L, Hutchinson M
Department of Pathology, Tufts University, Boston, Massachusetts 02111.
Anal Quant Cytol Histol. 1988 Feb;10(1):10-5.
Image analysis techniques were used to characterize individual nuclei of cells and entire clusters of cells in hematoxylin-and-eosin-stained smears of fine needle aspirates of the breast to determine the ability of these techniques to distinguish benign from malignant cases. Analysis of the individual nuclear features showed significant differences in nuclear area, shape (bending energy), texture and integrated darkness between benign and malignant samples. Analysis of the clusters demonstrated that the benign clusters were fewer in number, more cellular (average gray level) and larger than malignant clusters. A statistical classifier was constructed to test the discriminatory accuracy for benign and malignant cases. Good discrimination was found for both the individual nuclei and the clusters when analyzed separately, although a few cases were misclassified by each type of analysis. When combined, the two classifiers achieved a completely accurate classification. This suggests the complementary nature of high-resolution single-cell analysis and the more global cluster analysis techniques.
图像分析技术用于表征乳腺细针穿刺苏木精-伊红染色涂片上的单个细胞核和整个细胞簇，以确定这些技术区分良性和恶性病例的能力。对单个核特征的分析表明，良性和恶性样本在核面积、形状（弯曲能量）、纹理和积分暗度方面存在显著差异。对细胞簇的分析表明，良性细胞簇数量较少，细胞更多（平均灰度），且比恶性细胞簇更大。构建了一个统计分类器来测试良性和恶性病例的判别准确性。单独分析时，对于单个细胞核和细胞簇都发现了良好的判别效果，尽管每种分析类型都有一些病例被误分类。当两者结合时，两个分类器实现了完全准确的分类。这表明了高分辨率单细胞分析和更全面的细胞簇分析技术的互补性。
