Neuromedical Systems, Inc.

Itt Corporation

Nueromedical Systems, Inc.

Autocyte North Carolina, L.l.c.

Mark R Rutenberg

Laurie J Mango

Thomas L Hall

Mathilde Elisabeth Boon

Lanbrecht Piet Kok

Akiva Rutenberg

Leonid Strinkovsky

Robert Domes

Randall L Luck

Richard Chaban

James Knapp

Thomas Dulak

James C Herriman

James M Herriman

Robert Tjon-Fo-Sang

John Porzio

An automated screening system and method for cytological specimen classification in which a neural network is utilized in performance of the classification function. Also included is an automated microscope and associated image processing circuitry.

Automated cytological specimen classification system and method

A semi-automated method of classifying a histological specimen for the presence of premalignant or malignant cells, including the steps of treating a specimen with an immunohistochemical marker to provide a visual indication of cell proliferation, ranking individual objects in the specimen in an order according to the likelihood that each object has attributes consistent with a premalignant or malignant cell or cell cluster, selecting for display a set of said objects according to said ranking, and displaying images of said selected objects to facilitate review by an operator.

Automated histological specimen classification system and method

A method of determining the reliability of a cytological screening test including the steps of viewing at least part of a cytological specimen, creating an image of the view, producing a digital representation of the image, ranking objects in the digital representation based on the degree to which each respective object has characteristics more likely found in a typical premalignant or malignant cell than in a typical benign cell, and displaying for review by an operator the images of at least a minimum number of the highest ranked objects such that objects of a predetermined cell type are displayed in the absence of premalignant or malignant cells, wherein the detection by the operator of the presence in the display of at least one cell of the predetermined cell type tends to indicate that the test is reliable.

A device for the visual inspection of a specimen, comprising a first microscope for obtaining a magnified view of different areas of a specimen, a display monitor for displaying images of at least a subset of the different areas of the specimen, selection means for enabling the selection of a image displayed on the monitor, a second microscope for review of an area of the specimen corresponding to the selected image, a motorized stage for positioning said specimen with respect to the field of view of the second microscope, and a processor for determining the image selected and instructing the motorized stage to position the specimen so that the area of the specimen corresponding to the selected image is in the field of view of the second microscope.

Device and method for facilitating inspection of a specimen

Automated specimen classification system and method

A method of checking a process of individually inspecting successive sets of one or more images displayed on a screen, includes the steps of displaying at least one predetermined image having at least one area of interest for which inspection is to be performed, temporarily preventing a new set of at least one image from being displayed, recording each time that an event occurs at a predetermined location in such at least one image, and permitting a new set of at least one image to be displayed after the event has been recorded as having occurred at all predetermined areas of interest in the current display.

Inspection apparatus and method with inspection auditing for images

Neural network based automated cytological specimen classification

The subject invention employs a system integrated fault-tree analysis (SIFTAN) which has the unique ability to detect all latent hardware and software design defects that could cause unanticipated critical failure of a complex software controlled electronic system. This new approach modifies and then integrates two existing system analysis techniques-namely, hardware fault-tree analysis (HFTA) and software fault-tree analysis (SFTA). The resultant integrated technique is identified as SIFTAN for system integrated fault-tree analysis. Through its integrated hardware/software scope and its critical failure focus, SIFTAN has unique potential to solve the essential analytical limitation behind the software reliability problem. The system exceeds the scope of all current system analysis techniques by providing a system free from all potential critical specification hardware or software design errors. The system accomplishes the above-noted objects by performing fault tree analysis with respect to the contents of a dynamic 'stack of contradiction parameters' and then superimposing the modified hardware and software fault trees onto each other. The super position is accomplished by automatically branching from the software to a specified fault tree hardware whenever hardware could result in a critical system output. It is important to indicate that the SIFTAN system is applied with great advantages to early conceptual levels of system design in addition to its certification of the final design implementation.

System integrated fault-tree analysis methods (SIFTAN)

Growing community of inventors

Monsey, NY, United States of America

Mark R Rutenberg