Abstract: Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.

Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

Abstract: Methods and systems for generating inspection results for a specimen with an adaptive nuisance filter are provided. One method includes selecting a portion of events detected during inspection of a specimen having values for at least one feature of the events that are closer to at least one value of at least one parameter of the nuisance filter than the values for at least one feature of another portion of the events. The method also includes acquiring output of an output acquisition subsystem for the sample of events, classifying the events in the sample based on the acquired output, and determining if one or more parameters of the nuisance filter should be modified based on results of the classifying. The nuisance filter or the modified nuisance filter can then be applied to results of the inspection of the specimen to generate final inspection results for the specimen.

Abstract: One embodiment relates to a method of inspecting a site location on a target substrate. Contours are obtained, the contours having been generated from a reference image using a design clip. A target image of the site location is acquired. The contours are aligned to the target image, and contrast values are computed for pixels on the contours. A threshold is applied to the contrast values to determine contour-based defect blobs. Another embodiment relates to a method of generating contours for use in inspecting a site location for defects. Other embodiments, aspects and features are also disclosed.

Abstract: A method of contrast enhancement for improved visualization of diagnostically important tissue structures, such as blood vessels. A texture analysis algorithm is applied to identify regions with a high likelihood of disease. Mathematical morphology operations are applied to identify areas of high and low brightness (intensity). The low intensity areas are then subtracted, and controllably variable amounts of the high intensity areas are added, controlled by a selectable tuning parameter, to produce an image with controllably variable visualization enhancement.

Abstract: One embodiment relates to a method of inspecting a site location on a target substrate. Contours are obtained, the contours having been generated from a reference image using a design clip. A target image of the site location is acquired. The contours are aligned to the target image, and contrast values are computed for pixels on the contours. A threshold is applied to the contrast values to determine contour-based defect blobs. Another embodiment relates to a method of generating contours for use in inspecting a site location for defects. Other embodiments, aspects and features are also disclosed.

Abstract: The present invention discloses a method for the detection of atypical vessels in digital cervical imagery. A pre-processing stage is applied to enhance the contrast of blood vessel features compared to the surrounding tissue. Next, a segmentation stage is applied to identify regions of interest for atypical vessels using texture and gradient information. Finally, a post-processing stage (1) identifies other clinically relevant features in the cervical imager, and removes these features from the region of interest; and (2) uses color, size, and shape information to further refine the region of interest to eliminate false positives and determine a final region of interest. This automated method of atypical vessel detection is especially useful for diagnostic purposes such as cervical cancer detection.

Abstract: A method of contrast enhancement for improved visualization of diagnostically important tissue structures, such as blood vessels. A texture analysis algorithm is applied to identify regions with a high likelihood of disease. Mathematical morphology operations are applied to identify areas of high and low brightness (intensity). The low intensity areas are then subtracted, and controllably variable amounts of the high intensity areas are added, controlled by a selectable tuning parameter, to produce an image with controllably variable visualization enhancement.

Abstract: The present invention discloses a method for the detection of atypical vessels in digital cervical imagery. A pre-processing stage is applied to enhance the contrast of blood vessel features compared to the surrounding tissue. Next, a segmentation stage is applied to identify regions of interest for atypical vessels using texture and gradient information. Finally, a post-processing stage (1) identifies other clinically relevant features in the cervical imager, and removes these features from the region of interest; and (2) uses color, size, and shape information to further refine the region of interest to eliminate false positives and determine a final region of interest. This automated method of atypical vessel detection is especially useful for diagnostic purposes such as cervical cancer detection.