Abstract
Archiving large sets of medical or cell images in digital libraries may require ordering randomly scattered sets of image data according to specific criteria, such as the spatial extent of a specific local color or contrast content that reveals different meaningful states of a physiological structure, tissue, or cell in a certain order, indicating progression or recession of a pathology, or the progressive response of a cell structure to treatment. Here we used a Self Organized Map (SOM)-based, fully automatic and unsupervised, classification procedure described in our earlier work and applied it to sets of minimally processed grayscale and/or color processed Scanning Electron Microscopy (SEM) images of CD4+ T-lymphocytes (so-called helper cells) with varying extent of HIV virion infection. It is shown that the quantization error in the SOM output after training permits to scale the spatial magnitude and the direction of change (+ or -) in local pixel contrast or color across images of a series with a reliability that exceeds that of any human expert. The procedure is easily implemented and fast, and represents a promising step towards low-cost automatic digital image archiving with minimal intervention of a human operator.
Abstract (translated)
在数字图书馆中归档大量的医学或细胞图像可能需要根据特定的标准随机排列分散的图像数据集，例如特定局部颜色的空间范围或对比度内容，以一定的顺序显示生理结构、组织或细胞的不同有意义状态，这表明病理学的进展或衰退，或细胞结构对治疗的渐进反应。在这里，我们使用了我们先前工作中描述的基于自组织图（SOM）的全自动无监督分类程序，并将其应用于具有不同程度HIV病毒感染的CD4+T淋巴细胞（所谓辅助细胞）的一组最小处理灰度和/或彩色处理扫描电子显微镜（SEM）图像。结果表明，训练后SOM输出的量化误差允许对一系列图像的局部像素对比度或颜色的空间大小和变化方向（+or-）进行缩放，其可靠性超过任何人类专家。该程序易于实施且速度快，是实现低成本自动数字图像归档的一个很有希望的步骤，而人工操作人员的干预最少。
URL
https://arxiv.org/abs/1905.03700
