Garcia G L, Kuklinski W S, Zahniser D J, Oud P S, Vooys P G, Brenner J F
Cytometry. 1987 Mar;8(2):210-6. doi: 10.1002/cyto.990080215.
A procedure for automated analysis of cervical smears has been implemented in an image cytometry system. Smears are described exclusively in terms of global and contextual information extracted by pattern-recognition algorithms and represented by a vector of proportions of cellular object types. Linear discriminant functions, based on a Fisher criterion, are derived to classify smears with a cross-section of diagnoses into two broad categories, normal and abnormal. Results obtained from 83 smears indicate 78% correct classification. In contrast to most automated systems, good classification results were obtained in normal smears with benign changes caused by inflammation and with postmenopausal atrophia and in abnormals with mild dysplasia. These findings suggest that contextual analysis may be sensitive to subtle changes in cellular morphology and to progressive patterns of dysplasia. When used with standard isolated cell analysis, contextual analysis may provide additional complementary information for automated cervical prescreening.
一种用于宫颈涂片自动分析的程序已在图像细胞计数系统中实现。涂片仅根据通过模式识别算法提取的全局和上下文信息进行描述，并由细胞对象类型比例向量表示。基于费希尔准则推导线性判别函数，以将具有诊断横截面的涂片分为正常和异常两大类。从83份涂片中获得的结果表明分类正确率为78%。与大多数自动化系统不同，在因炎症引起良性改变的正常涂片中、绝经后萎缩的涂片中以及轻度发育异常的异常涂片中都获得了良好的分类结果。这些发现表明，上下文分析可能对细胞形态学的细微变化和发育异常的进展模式敏感。当与标准的分离细胞分析一起使用时，上下文分析可能为宫颈自动预筛查提供额外的补充信息。
