Abstract: An optical tomography system for imaging an object of interest including a light source for illuminating the object of interest with a plurality of radiation beams. The object of interest is held within an object containing tube such that it is illuminated by the plurality of radiation beams to produce emerging radiation from the object containing tube, a detector array is located to receive the emerging radiation and produce imaging data used by a mechanism for tracking the object of interest.

Abstract: A system and method for detecting poor quality images in an optical tomography system includes an acquisition apparatus for acquiring a set of pseudo-projection images of an object having a center of mass, where each of the set of pseudo-projection images is acquired at a different angle of view. A reconstruction apparatus is coupled to receive the pseudo-projection images, for reconstruction of the pseudo-projection images into 3D reconstruction images. A quality apparatus is coupled to receive the 3D reconstruction images and operates to detect of selected features that characterize poor quality reconstructions.

Abstract: A method for reconstructing three-dimensional (3D) tomographic images. A set of pseudo-projection images of an object is acquired. Error corrections are applied to the set of pseudo-projection images to produce a set of corrected pseudo-projection images. The set of corrected pseudo-projection images are processed to produce (3D) tomographic images.

Abstract: Correcting pattern noise projection images includes acquiring a set of projection images with an optical tomography system including a processor, where each of the set of projection images is acquired at a different angle of view. A threshold is applied to each projection image produce a set of threshold images. Each threshold image may optionally be dilated to produce a set of dilated images that are summed to form an ensemble image. Each of the dilated images is processed to produce a set of binary images. The set of binary images are summed to form an ensemble mask. The ensemble image is divided by the ensemble mask to yield a background pattern noise image. Each projection image is multiplied by a scaling factor and divided by the background pattern noise to produce a quotient image that is filtered to produce a noise corrected projection image.

Abstract: A method for reconstructing three-dimensional (3D) tomographic images. A set of pseudo-projection images of an object is acquired. Error corrections are applied to the set of pseudo-projection images to produce a set of corrected pseudo-projection images. The set of corrected pseudo-projection images are processed to produce (3D) tomographic images.

Abstract: A system for collecting images at multiple wavelengths for image analysis of biological specimens. The process separates contrasts for images of a biological specimen prepared with a chromogen. Light wavelengths matched with each chromogen optimize separation for each image. Computing attenuation coefficients and extinction coefficients for each chromogen provides for determination of the concentration of each chromogen. A chromogen separator allows for subtracting predetermined chromogens to generate new images and aid in identification of cell morphology. The system can perform the image analysis of the new images using image processing techniques such as segmentation, feature calculation and object classification. At each stage of image processing, data from each of the new images may influence the processing of the other images.

Abstract: Dynamic control of the processing flow of an image analyzer such as a biological specimen analyzer as processing proceeds. Data collected and processed from a specimen under analysis, such as a biological specimen on a microscope slide, determines the fate of further processing. If there is enough evidence, based on the data collected from a slide, to make a decision with sufficient confidence, the processing of the slide can be stopped and a decision may be rendered. By avoiding unnecessary additional computation system throughput may be enhanced. Otherwise, data collection and computation continues until either certain termination criteria are met or no more data is left to acquire. This slide-dependent control and decision making method flexibly limits the amount of computation required to reach a system decision about a specimen.

Abstract: A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

Abstract: A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

Abstract: A thick group of cells classifier. Image data acquired from an automated microscope from a cytological specimen is processed by a computer system. The computer applies filters at different stages. Obvious artifacts are eliminated from analysis early in the processing. The first stage of processing is image segmentation where objects of interest are identified. The next stage of processing is feature calculation where properties of each segmented thick group object are calculated. The final step is object classification where every segmented thick group object is classified as being abnormal or as belonging to a cellular or non-cellular artifact.

Abstract: A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

Abstract: The invention detects areas of interest at low magnification, locating possible abnormal cells or other cells of interest using image processing and statistical pattern recognition techniques. Next, at high magnification, the areas identified at low magnification are re-examined. The information from the low magnification and high magnification scans is collated and a determination is made about the slide--whether it is normal, abnormal, contains endocervical component, and so forth. The invention also provides a method and apparatus to train object feature and slide feature classifiers. The invention provides an automated cytology system that can process training slides for use in a feed back classifier development environment. The invention also can classify endocervical groups of cells.

Abstract: A biological specimen classification strategy employs identification and integration of multiple cell patterns. An automated microscope acquires an image of a biological specimen such as a Pap smear and provides an image output to biological classifiers. The classifiers independently detect and classify a number of specimen types and provide classifications to an output field of view integrator. The integrator integrates the classifications. The integrated output then determines whether the classifiers should be reapplied to the image.

Abstract: A classifier measures robustness responsive to object presentation effects and decision boundary effects. A cytological image analysis computer obtains objects of interest and classifies them responsive to a decision tree classifier. The robustness of classification is calculated dynamically as objects are classified responsive to a segmentation robustness and a classification decisiveness measure. The results of the decisiveness measure and the segmentation robustness data are combined to provide enhanced overall classification reliability.