Abstract: A method and apparatus for optimizing biological and cytological specimen screening and diagnosis. A slide review process is recommended for cytological specimen screening to identify abnormal sub-populations for further review and also diagnosis by a human expert. An automated screener processes a cytological specimen. Using a slide score generated by the automated screener, a slide review process using a slide score classification is determined. The recommendation of slide review processes improves overall performance of the screening process as measured by sensitivity to abnormal specimens, and at the same time reduces the work load of a human reviewer. The system also effectively and smoothly integrates the process of initial screening of the specimen with the process of further review of the specimen and final diagnosis of the specimen.

Abstract: An incremental concurrent learning method starts with providing potential defects and fabrication information and a primary classification rule and secondary classification rule selection from a knowledge defect database from multiple products with different process cycles. The method then performs a truth inquiry to update a classification rule database for use by the primary classification rule and secondary classification rule selection. The method performs a primary defect classification and checks the confidence of the classification, and performs a secondary defect classification if the confidence is not high. If the confidence of the secondary defect classification is not high, a new defect may have been discovered and a novelty defect detection step is performed to define artifacts or potential new defect types to provide information for the truth inquiry.

Abstract: A method for checking cytological system illumination including the steps of checking global illumination variation, static field uniformity, dynamic field uniformity, specimen thickness variation, strobe repeatability, calibration plate cleanliness, and strobe dropout. A calibration plate and test target is employed for various illumination checks.

Abstract: A method for checking cytological system illumination including the steps of checking global illumination variation, static field uniformity, dynamic field uniformity, specimen thickness variation, strobe repeatability, calibration plate cleanliness, and strobe dropout. A calibration plate and test target is employed for various illumination checks.

Abstract: A method for checking cytological system illumination including the steps of checking global illumination variation, static field uniformity, dynamic field uniformity, specimen thickness variation, strobe repeatability, calibration plate cleanliness, and strobe dropout. A calibration plate and test target is employed for various illumination checks.

Abstract: A method for checking cytological system illumination including the steps of checking global illumination variation, static field uniformity, dynamic field uniformity, specimen thickness variation, strobe repeatability, calibration plate cleanliness, and strobe dropout. A calibration plate and test target is employed for various illumination checks.

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 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: Improving accuracy of frequency response measurements of linear systems using modulation transfer function test compensation. An optical imaging system illuminates and images an input test pattern. A processor measures a modulation transfer function. The processor determines a compensating factor for error in the input test pattern duty cycle. The driving function for the measurement is a test pattern having a periodic waveform approximating a square wave with an error in duty cycle. A windowed fast fourier transform on a greyscale image of the test pattern generates odd harmonics. Adjusting amplitude values of the odd harmonics corrects for the error in resolution target duty cycle.

Abstract: Measurement of the optical transfer function of an optical system imaging a bar pattern. A one dimensional fast Fourier transform processes sampled image data from the bar pattern. A model of the system utilizes robust measurements of the period, duty cycle and center of each stripe in the pattern. A signal alias free optical transfer function is estimated from the plurality of one dimensional frequency representation of the signal. An idealized bar pattern synthesized from the measured parameters of period, duty cycle and the center of each stripe in the pattern generates an ideal optical transfer function. The noise reduced optical transfer function, OTF, is estimated from OTF of the signal and OTF of the synthesized bar pattern.

Abstract: A high speed image capture apparatus includes a camera with an integration period between consecutive flashes of an illuminating light pulse, where the camera has an image read out period and an image output. A timer for provides a strobe output representative of the start of a flash. An image capture apparatus captures a first image during the integration period and a second image within a predetermined time after capturing the first image but before the image read out period has elapsed so as to substantially increase a rate of producing usable image outputs. The image capture apparatus is coupled to the strobe output and the image output.

Abstract: An automated method for checking cytological system autofocus integrity. The automated method includes the steps of checking focus illumination integrity, checking focus camera Modulation Transfer Function, checking focus camera position integrity, and checking closed loop accuracy. Checking focus illumination integrity includes checking focus illumination system integrity, and checking a focus noise floor level. Checking focus camera position integrity includes checking focus camera longitudinal separation, and checking focus camera lateral separation. Checking focus camera position integrity includes checking focus filter frequency response.

Abstract: A computer receives image data from a star-shaped optical target in the object plane and calculates angle-dependent boundary sharpness. The horizontal, x-direction, amplitude derivative and the vertical, y-direction, amplitude derivative are computed over a portion of each star pattern image from a Z panning sequence. A microscope slide stage, carrying the target, is moved vertically from a level just below where the target is in focus to a level just above where the target is in focus. For each small increment of vertical motion, Z panning, an image of the star pattern is captured for analysis. Computations are performed on the differentiated images to search for evidence of elongation of the point spread function and variation with stage Z position of the angle of long axis of such an out-of-round point spread function. The presence of a distorted point spread function that varies along the optical axis indicates astigmatism.

Abstract: An automated method for checking cytological system autofocus integrity. The automated method includes the steps of checking focus illumination integrity, checking focus camera Modulation Transfer Function, checking focus camera position integrity, and checking closed loop accuracy. Checking focus illumination integrity includes checking focus illumination system integrity, and checking a focus noise floor level. Checking focus camera position integrity includes checking focus camera longitudinal separation, and checking focus camera lateral separation. Checking focus camera position integrity includes checking focus filter frequency response.

Abstract: A suite of tests and parameter monitoring methods for at least five major subsystems that are found in most automated image processing systems, as well as calibration routines for three major system functions. The five areas of subsystem verification include processing quality, illumination quality, image collection quality, autofocus quality, and position quality. An automated biological specimen analysis system uses self measures of system parameters to provide a system status. The system status is reported to the user.

Abstract: A method for identifying the size, shape, and location of objects in a specimen where the image of the specimen is represented by image data and where the image data is processed to provide mask data representing a mask where the mask identifies the size, shape, and location of the object. Generally, the method includes the step of enhancing the image and creating a threshold image where the threshold image includes a threshold intensity value associated with each pixel of the image. The threshold image is combined with the original image to provide a mask image that identifies the size, shape, and location of the objects. The mask image may be further refined to ensure accurate identification of the object. Various other techniques are disclosed within the general method for processing image data.

Abstract: A computer receives image data from a star-shaped optical target in the object plane and calculates angle-dependent boundary sharpness. The horizontal, x-direction, amplitude derivative and the vertical, y-direction, amplitude derivative are computed over a portion of each star pattern image from a Z panning sequence. A microscope slide stage, carrying the target, is moved vertically from a level just below where the target is in focus to a level just above where the target is in focus. For each small increment of vertical motion, Z panning, an image of the star pattern is captured for analysis. Computations are performed on the differentiated images to search for evidence of elongation of the point spread function and variation with stage Z position of the angle of long axis of such an out-of-round point spread function. The presence of a distorted point spread function that varies along the optical axis indicates astigmatism.

Abstract: An automated method for checking cytological system autofocus integrity. The automated method includes the steps of checking focus illumination integrity, checking focus camera Modulation Transfer Function, checking focus camera position integrity, and checking closed loop accuracy. Checking focus illumination integrity includes checking focus illumination system integrity, and checking a focus noise floor level. Checking focus camera position integrity includes checking focus camera longitudinal separation, and checking focus camera lateral separation. Checking focus camera position integrity includes checking focus filter frequency response.

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.