Zajicek G, Shohat M
Comput Biomed Res. 1983 Dec;16(6):553-62. doi: 10.1016/0010-4809(83)90041-1.
Bone marrow smears stained with Giemsa were scanned with a video camera under computer control. Red cells (89 normal and 44 megaloblastic) were sampled. Each cell was digitized into 70 X 70 pixels, each pixel representing a square area of 0.04 micron 2 in the original image. The pixel gray values ranged between 0-255. Zero stood for white and 255 represented black, while the numbers in between stood for the various shades of gray. The cells were first classified into six distinct classes, each representing a red blood cell differentiation state. The canonical discrimination functions derived from this classification were then utilized for grading cell differences in a continuous fashion. Each canonical discrimination function is represented in this multidimensional space by an orthogonal axis. The distance between two points (or cells) reflects their degree of similarity. By relating this measure to a reference state, it is possible to quantitate red blood cell differentiation changes. This approach is applicable to any differentiating tissue.
用吉姆萨染色的骨髓涂片在计算机控制下用摄像机进行扫描。采集红细胞（89例正常红细胞和44例巨幼红细胞）。每个细胞被数字化为70×70像素，每个像素在原始图像中代表0.04平方微米的方形区域。像素灰度值范围在0 - 255之间。0代表白色，255代表黑色，而中间的数字代表不同深浅的灰色。细胞首先被分为六个不同的类别，每个类别代表一种红细胞分化状态。然后利用从该分类中得出的典型判别函数以连续方式对细胞差异进行分级。每个典型判别函数在这个多维空间中由一个正交轴表示。两点（或细胞）之间的距离反映了它们的相似程度。通过将这个度量与一个参考状态相关联，就有可能对红细胞分化变化进行定量。这种方法适用于任何正在分化的组织。
