Nuffer Lisa L, Medvick Patricia A, Foote Harlan P, Solinsky James C
Pacific Northwest National Laboratory, Applied Computer Science, Richland, WA 99352, USA.
Cytometry A. 2006 Aug 1;69(8):897-903. doi: 10.1002/cyto.a.20294.
Microscopes form projected images from illuminated objects, such as cellular tissue, which are recorded at a distance through the optical system's field of view. A telescope on a satellite or airplane also forms images with a similar optical projection of objects on the ground. Typical visible illuminations form a displayed set of three-color channels (Red Green Blue [RGB]) that are combined from three image sensor arrays (e.g., focal plane arrays) into a single pixel coding for each color present in the image. Analysis of these RGB color images develops a qualitative image representation of the objects.
Independent component analysis (ICA) is used for analysis and enhancement of multispectral images, and compared with the similar and widely used principal component analysis.
The data examples indicate that the ICA enhancement, and the resulting RGB image combination display, can be useful in processing datacubes of cellular data where isolation of unknown subtle image elements representing objects is desired.
ICA image enhancement can aid processing of datacubes of cellular data by clarifying subtle image elements. These parallelizable algorithms can be implemented for real-time, online analysis.
显微镜通过照亮诸如细胞组织等物体来形成投影图像，这些图像通过光学系统的视场在一定距离处被记录下来。卫星或飞机上的望远镜也通过对地面物体进行类似的光学投影来形成图像。典型的可见光照明形成一组显示的三色通道（红、绿、蓝[RGB]），这些通道由三个图像传感器阵列（例如焦平面阵列）组合成图像中每种颜色的单个像素编码。对这些RGB彩色图像的分析形成了物体的定性图像表示。
独立成分分析（ICA）用于多光谱图像的分析和增强，并与类似且广泛使用的主成分分析进行比较。
数据示例表明，ICA增强以及由此产生的RGB图像组合显示，在处理细胞数据的数据立方体时可能很有用，在这种情况下，需要分离代表物体的未知细微图像元素。
ICA图像增强可以通过澄清细微图像元素来辅助细胞数据的数据立方体处理。这些可并行化算法可以实现实时在线分析。
