Dr. Mehrnoush Khojasteh Profile

Dr. Mehrnoush Khojasteh

Image Scientist at Ventana Medical Systems Inc

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Proceedings Article | 25 February 2010 Paper

Selective excitation light fluorescence (SELF) imaging

Mehrnoush Khojasteh, Calum MacAulay

Proceedings Volume 7568, 75680F (2010) https://doi.org/10.1117/12.843739

KEYWORDS: Luminescence, Imaging systems, Tissues, Composites, RGB color model, Diagnostics, Optical filters, Principal component analysis, Light sources, Absorption

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Fluorescence imaging is a potential candidate for tissue diagnostics in a wide variety of clinical situations. In order to extract diagnostic information using fluorescence, different approaches may be used. Typically, fluorescence imaging is performed by illuminating the sample at a single excitation wavelength and detecting the emissions at one or more wavelengths. We have built a prototype system for a new fluorescence imaging technique denoted Selective Excitation Light Fluorescence (SELF) Imaging. In this technique, the sample is illuminated with multiple excitation wavelengths, and one or more emitted wavelength images are detected. By using a multitude of illumination wavelengths or a weighted sum of illumination wavelengths, SELF imaging can highlight differences in the excitation spectra of fluorophores in the sample. Some potential advantages of this imaging technique are: detection of multiple labeled objects in microscopy using only a single filter cube, increasing the number of simultaneous labels which can be used on a single slide as labels are separated by their absorption spectra not just their emission spectra, detection of different components of tissue based on different excitation spectra, etc.

Proceedings Article | 19 February 2007 Paper

Hyperspectral imaging and spectral unmixing of stained tissue sections using a spectrally programmable light engine

N. MacKinnon, M. Khojasteh, P. Lane, C. MacAulay, M. Guillaud, U. Stange

Proceedings Volume 6441, 64410A (2007) https://doi.org/10.1117/12.702159

KEYWORDS: Tissues, Hyperspectral imaging, Tissue optics, Imaging systems, Control systems, RGB color model, Optical filters, Image segmentation, Image filtering, Lamps

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A series of hyperspectral transmission images of hematoxylin and eosin stained tissue sections from cervical biopsies were acquired at 10 nm intervals and assembled into a hyperspectral image cube. Custom software providing extraction of spectra at each pixel allows selection of images with maximum contrast for determination of selected features and differentiation of tissue features. Illumination profiles were created using a spectrally and temporally programmable light engine based on a spatial light modulator that can dynamically create any narrow or broadband spectral profile was used to select illumination wavelengths. Images were acquired with a monochrome CCD camera. Several methods of combining images from individual or composite spectral bands to recreate color images for pathologist review are shown. Unlike current "mechanical" illumination systems employing optical filters, filter wheels, motors, shutters and multiple control interfaces, the light engine integrates the lamp, wavelength control, intensity control and exposure control in a simple MEMS based system, where the only moving part is the lamp cooling fan. Illumination can now be programmed dynamically with digital control of all illumination parameters allowing wavelengths and intensities to be changed much faster than with filter wheels, and providing exposure control orders of magnitude more precise than mechanical shutters. This system can be integrated with digital imaging systems. Digitally controlled illumination is bit additive with image data providing high dynamic range imaging with monochrome or with color imaging devices. Performance of image analysis software for nuclear morphometric and tissue architecture analysis are compared for different wavelength regions.

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