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: The detection of cellular aggregates within cytologic samples. An image analysis system with an image gathering system includes a camera, a motion controller, an illumination system and an interface obtains images of cell groupings. The image gathering system is constructed for gathering image data of a specimen mounted on a slide and is coupled to a data processing system. Image data is transferred from the image gathering system to the data processing system. The data processing system obtains objects of interest. A four step process finds cellular aggregates. The first step is acquisition of an image for analysis. The second step is extraction of image features. The third step is classification of the image to determine if any potential cellular aggregates may exist in the image. The fourth step is segmentation of objects which includes the substeps of detecting and locating potential cellular aggregates.

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.

Abstract: A method and apparatus for determining whether a slide is suitable for processing. A suite of suitability tests are performed by an automated microscope system. The tests include magnification error flags, staining flags, main optical density flags, including detected intermediate cell nuclei, rings around detected intermediate cell nuclei, average texture measure of detected intermediate cell nuclei, average contrast to detected intermediate cell nuclei to cytoplasm, standard deviation of detected intermediate cell nuclei optical densities, detected intermediate cell ratios, average stripe area, measure of a saturated magnification of the image, measure of a grossly saturated magnification, and the percentage of images focused properly on a first try, including images never focused properly. The automated microscope quantifies the measurements in a reliable and repeatable way.

Abstract: An automated biological specimen screener reports an assessment of slide and specimen preparation and quality. The automated biological specimen screener measures, parameters which reflect slide physical characteristics, specimen collection quality, and specimen preparation quality. The automated system reports an objective measure and uses a consistent standard of evaluation. The automated system evaluates characteristics of a slide set from a clinic. The automated system makes a determination of whether these characteristics are within a training capability of a given automated biological screener. Additionally, rather than periodic reviews, slides successfully scanned by the automated system may be used as part of a specimen preparation assessment.

Abstract: A method for single image based image contrast quality evaluation is used in a computer controlled image system including an automated microscope for acquiring images. An image is acquired. An image contrast score is computed for the image. An image reliability score is computed for the image. The image contrast score and image reliability score are accumulated and combined to produce a contrast quality score. The contrast quality score is compared to a predetermined limit so as to produce a quality of processing result.

Abstract: An automated laboratory process monitoring method for a computer controlled automated cytology system initializes lab process assessment slide data so as to produce an initial batch of qualified slides. Monitor parameters are extracted from the initial batch of qualified slides so as to determine control limits. Field data is monitored by comparing the field data to the control limits.

Abstract: An automated biological screening system obtains biological and procedural data from a slide set of a selected clinical laboratory. The integration system tests the data on standardized criteria and passes and fails the data in selected categories. The results of the assessment are used to make process adjustment recommendations based on the results of a laboratory process adjustment procedure. Assessment and adjustment may continue until data from a slide set from the selected clinical laboratory passes in each category. The integration system then sets up, calibrates and installs the automated biological screening system. During operation, the integration system continuously monitors biological data generated by the automated biological screening system. The biological data may also be stored in a central product/service database for additional monitoring. The integration system also serves as an objective standard for reviewing and improving laboratory practices.