Thermo Electron Scientific Instruments Corporation

Alexander Grenov

Francis Jerome Deck

Damian W Ashmead

Kevin K Kim

Federico Izzia

Chris Xavier Kauffold

Michael S Bradley

William Joseph McCarthy

Kathleen J Schulting

Gary L Ritter

Kathleen A Larson

Garry L Ritter

An image analysis system includes a video camera that collects YUV color images of a liquid sample disposed between a capital and a pedestal, the color images being collected while a light source shines light through an optical beam path between the capital and the pedestal, and a processor adapted to i) obtain from the YUV color images a grayscale component image and a light scatter component image, and ii) obtain at least one binary image of the grayscale component image and at least one binary image of the light scatter component image.

Image analysis system and method

MCR provided estimated pure component time series spectra as extracted from infrared or other spectroscopy is capable of being compared to spectra in a reference library to find the best matches. The best match spectra can then each in turn be combined with the reference spectra, with the combinations also being screened for best matches versus any one of the estimated pure component time series spectra. These resulting best matches can then also undergo the foregoing combination and comparison steps. The process can repeat in this manner in an unbounded fashion if desired until an appropriate stopping point is reached, for example, when a desired number of best matches are identified, when some predetermined number of iterations has been performed, etc. This methodology is able to return best-match spectra with far fewer computational steps and greater speed than if all possible combinations of reference spectra are considered.

Multi-component regression/multi-component analysis of time series files

An unknown spectrum obtained from infrared or other spectroscopy can be compared to spectra in a reference library to find the best matches. The best match spectra can then each in turn be combined with the reference spectra, with the combinations also being screened for best matches versus the unknown spectrum. These resulting best matches can then also undergo the foregoing combination and comparison steps. The process can repeat in this manner until an appropriate stopping point is reached, for example, when a desired number of best matches are identified, when some predetermined number of iterations has been performed, etc. This methodology is able to return best-match spectra (and combinations of spectra) with far fewer computational steps and greater speed than if all possible combinations of reference spectra are considered.

Efficient spectral matching, particularly for multicomponent spectra

In a spectroscopic microscope, a video image of a specimen is analyzed to identify regions having different appearances, and thus presumptively different properties. The sizes and locations of the identified regions are then used to position the specimen to align each region with an aperture, and to set the aperture to a size appropriate for collecting a spectrum from the region in question. The spectra can then be analyzed to identify the substances present within each region of the specimen. Information on the identified substances can then be presented to the user along with the image of the specimen.

Spectroscopic microscopy with image-driven analysis

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Madison, WI, United States of America

Alexander Grenov