Albertini Maria C, Teodori Laura, Piatti Elena, Piacentini Maria P, Accorsi Augusto, Rocchi Marco B L
Istituto di Chimica Biologica "G. Fornaini", Università degli Studi di Urbino, Urbino, Italy.
Cytometry A. 2003 Mar;52(1):12-8. doi: 10.1002/cyto.a.10019.
Modification of erythrocyte morphology is clinically important in hematology and medicine. Its detection is routinely performed by subjective microscopic evaluation, which is difficult and strongly dependent on the operator's expertise. We developed an original automated methodology to analyze erythrocyte cell shape modification to support and improve the operator's capability and expedite measurements.
We used morphometric parameters derived from optical microscope images elaborated with an image processing software (NIH Scion Image) to construct a new application for statistical multivariate discriminant analysis.
For each cell type the elaboration of the morphometric parameters allowed us to develop a chromogenic index, a dimension index, a biconcavity index, and a density profile. The measurements of these indexes were used to construct a statistical methodology that could discriminate among erythrocyte morphologies according to Bessis. When applied casewise, the model effectively differentiated between discocytes, target cells, ovalocytes, macrocytes, and microcytes, with an agreement of 70% between actual and predicted classifications.
The results clearly demonstrated that a set of opportunely selected morphometric parameters derived from optical microscope images and statistically analyzed can effectively discriminate with a high degree of certainty among different shape modifications that red blood cells can undergo in various in vitro and in vivo conditions. This method represents the first attempt to automate the definition of erythrocyte morphology and may have important applications in cases in which the detection of erythrocyte cell shape changes is crucial.
红细胞形态的改变在血液学和医学中具有重要的临床意义。其检测通常通过主观显微镜评估进行，这既困难又强烈依赖于操作人员的专业知识。我们开发了一种原创的自动化方法来分析红细胞形态的改变，以支持和提高操作人员的能力并加快测量速度。
我们使用从光学显微镜图像中获取的形态测量参数，这些图像由图像处理软件（美国国立卫生研究院Scion Image）进行处理，以构建一种用于统计多变量判别分析的新应用程序。
对于每种细胞类型，形态测量参数的处理使我们能够开发出一个显色指数、一个尺寸指数、一个双凹指数和一个密度分布图。这些指数的测量结果被用于构建一种统计方法，该方法可以根据贝西（Bessis）对红细胞形态进行区分。当逐例应用时，该模型有效地区分了双凹圆盘状红细胞、靶形细胞、椭圆形红细胞、大红细胞和小红细胞，实际分类与预测分类之间的一致性为70%。
结果清楚地表明，从光学显微镜图像中适当选择并经过统计分析的一组形态测量参数，可以高度准确地有效区分红细胞在各种体外和体内条件下可能经历的不同形状改变。该方法代表了对红细胞形态定义进行自动化的首次尝试，在红细胞形态变化检测至关重要的情况下可能具有重要应用。
