Digital microscope

Abstract: Apparatus for and method of fully automatic rapid scanning and digitizing of an entire microscope sample, or a substantially large portion of a microscope sample, using a linear array detector synchronized with a positioning stage that is part of a computer controlled microscope slide scanner. The invention provides a method for composing the image strips obtained from successive scans of the sample into a single contiguous digital image. The invention also provides a method for statically displaying sub-regions of this large digital image at different magnifications, together with a reduced magnification macro-image of the entire sample. The invention further provides a method for dynamically displaying, with or without operator interaction, portions of the contiguous digital image. In one preferred embodiment of the invention, all elements of the scanner are part of a single-enclosure that has a primary connection to the Internet or to a local intranet. In this embodiment, the preferred sample type is a microscope slide and the illumination and imaging optics are consistent with transmission mode optics optimized for diffraction-limited digital imaging.

Abstract: An image scanner for microscopic objects. The image scanner has a microscope with a high precision computer controlled motor driven stage to provide X,Y plane displacements in order to scan microscopic objects under the microscope. There is an image sensor and a digitizer in association with the microscope to sense a horizontal image line or a two dimensional image and provide a digital representation of the line or image. A digital signal processor processes digitized signals from the sensor. There is a computer to control the mechanical and electronic scanning and to store and display information from the digital signal processor. Methods of scanning a microscopic object are also described. The methods comprise positioning the object on a motorized stage of a microscope having an image sensor in a focal plane. The object is scanned and signals received from the sensor during scanning are digitized. The digitized signals are processed with a digital signal processor in order to automatically recognize objects while the sample is being scanned. The processed information contained in the signals and the location coordinates of objects are stored. The methods include automatical revisiting of recognized objects for further analysis.

Abstract: Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, and computational processing of digitized images. Although many of the latest digital signal processing techniques have been successfully applied to compress, restore, and register digital microscope images, automated approaches for recognition and understanding of complex subcellular patterns in light microscope images have been far less widely used. We describe a systematic approach for interpreting protein subcellular distributions using various sets of subcellular location features (SLF), in combination with supervised classification and unsupervised clustering methods. These methods can handle complex patterns in digital microscope images, and the features can be applied for other purposes such as objectively choosing a representative image from a collection and performing statistical comparisons of image sets.

Abstract: A method for detecting malignancy-associated changes. A sample of cells is obtained and stained to identify the nuclear DNA material. The sample is imaged with a digital microscope. Objects of interest are identified in the sample of cells based on the intensity of the pixels that comprise the object versus the average intensity of all pixels in the slide image. An exact edge is located for each object and variations in the illumination intensity of the microscope are compensated for. A computer system calculates feature values for each object and, based on the value of the features, a determination is made whether the cell exhibits malignancy-associated changes or not.