Background: 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. Methods: Independent component analysis (ICA) is used for analysis and enhancement of multispectral images, and compared with the similar and widely used principal component analysis.

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Multiple images acquired in real-time from a confocal microscope in different illumination

This paper reports on the Pacific Northwest National Laboratory (PNNL) DOE Initiative in Image Science and Technology (ISAT) research, which is developing algorithms and software tool sets for remote sensing and biological applications. In particular, the PNNL ISAT work is applying these research results to the automated analysis of realtime cellular biology imagery to assist the biologist in determining the correct data collection region for the current state of a conglomerate of living cells in three-dimensional motion. The real-time computation of the typical 120 MB/sec multi-spectral data sets is executed in a Field Programmable Gate Array (FPGA) technology, which has very high processing rates due to large-scale parallelism. The outcome of this artificial vision work will allow the biologist to work with imagery as a creditable set of dye-tagged chemistry measurements in formats for individual cell tracking through regional feature extraction, and animation visualization through individual object isolation/characterization of the microscopy imagery.

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