Photometric calibration for quantitative spectral microscopy under transmitted illumination.
Over the past decade, there have been significant developments in the mechanisms for examination of biological and material samples. These developments exploit techniques in light microscopy to elucidate specific parts of cells and tissues, as well as inorganic particles. In recent years, spectral microscopy has become more prevalent for characterization of samples. Simultaneously, sensor technology has progressed as well and modern charge-coupled devices (CCD) cameras are now capable of achieving high spatial resolution and high sensitivity measurements of signals in the optical microscope. One major impediment in obtaining absolute quantitative information of imaged samples is the lack of automated photometric calibration mechanisms for spectral microscopes. In this paper, we present a methodology for achieving photometric calibration of an automated spectral imaging system targeted towards examination of biological samples. By acquiring spatial and spectral data simultaneously, spectral imaging allows one to exploit physical connections between a particle's morphology and its characteristic response to the optical spectrum. In composite biological material, the interpretation of the spectra is a complicated problem. This is because any light source and charge-coupled device camera used for data acquisition does not have a uniform illumination spectra and quantum efficiency, respectively, across the emitted light spectra. To balance the spectral response across individual wavelengths, our method modulates the exposure duration for the charge-coupled device camera during image acquisition. We present an image similarity based method to calibrate the system. Experiments to test the effectiveness of the calibration method under the various image similarity metrics are presented along with results to show the calibrated system's ability to accurately measure spectra based on the measured transmission profiles of optical filters.
Thigpen
J
J
Quantitative Imaging Laboratory, Department of Computer Science, University of Houston, 4800 Calhoun, 501 Philip G. Hoffman, Houston, TX 77204-3010, USA.
Merchant
F A
FA
Shah
S K
SK