Patent EP0336608A2 successfully refreshed from Google Patents.
EP0336608A2
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Neural network based automated cytological specimen classification system and method
Assignee: AutoCyte North Carolina LLC
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Filing: 1989-03-22
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Granted: —
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Expires: 2009-03-22
Abstract
Abstract 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.
Inventors
- 👤 Mark R. Rutenberg
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-11-04 09:41
CPC Classifications
G; G01; G01N; G01N15/00; G01N15/10; G01N15/14; G01N15/1468; G06; G06N; G06N3/00; G06N3/02; G06N3/04; G06N3/0499; G06N3/08; G06N3/09; G06V; G06V20/00; G06V20/60; G06V20/69; G06V20/693; G06V20/698; G16; G16H; G16H30/00; G16H30/40; G16H50/00; G16H50/20; G01N15/1429; G01N15/1433; G01N2015/1488; Y; Y10; Y10S; Y10S128/00; Y10S128/92; Y10S128/925; Y10S706/00; Y10S706/902; Y10S706/924
Claims Analysis
Total Claims: 14
Claim 1
Independent Claim
65 words
An automated cytological specimen classifier, comprising a) an automated microscope, b) a video camera-charge coupled device, c) an image digitizer, d) a primary statistical classifier for detection of objects in a cytological specimen which exceed a threshold integrated optical density, and e) a secondary classifier based on a neural network for detection of pre-malignant and malignant cells among the objects identified by the primary classifier.
Claim 2
Dependent Claim
65 words
An automated cytological specimen classifier, comprising a) an automated microscope, b) a video camera-charge coupled device, c) an image digitizer, d) a primary statistical classifier for detection of objects in a cytological specimen which exceed a threshold integrated optical density, and e) a secondary classifier based on a neural network for detection of pre-malignant and malignant cells among the objects identified by the primary classifier.
Claim 3
Dependent Claim
32 words
The automated cytological classifier of claim 1 further comprising a neural network pre-screening classifier for recognition and classification of general areas within a specimen that contain material other than a cellular monolayer.
Claim 4
Dependent Claim
27 words
The automated classifier of claim 2 wherein the output from the pre-screening classifier is utilized to exclude the said identified areas from analysis by the secondary classifier.
Claim 5
Dependent Claim
34 words
The automated classifier of claim 2 wherein the output from the pre-screening classifier is utilized to modify the secondary classification on images found within the areas of the specimen identified by said pre-screening classifier.
Claim 6
Dependent Claim
32 words
The automated classifier of claim 1 wherein the primary statistical classifier is restricted to evaluation of the cellular nucleus while the secondary classifier evaluates both the cellular nucleus and its surrounding cytoplasm.
Claim 7
Dependent Claim
24 words
The automated classifier of claim 1 wherein both the primary statistical classifier and the secondary classifier are restricted to evaluation of the cellular nucleus.
Claim 8
Dependent Claim
36 words
The automated classifier of claim 1 further comprising means for making an additional non-neural net classification of nuclear morphological components in addition to integrated optical density, said means being coupled between the primary and secondary classifier.
Claim 9
Dependent Claim
52 words
An automated cytological specimen classifier, comprising a low resolution neural network for performing primary classification of a cytological specimen, a high resolution neural network for performing secondary classification, and means for coupling said low and high resolution neural networks to pass data representing locations of interest from the former to the latter.
Claim 10
Dependent Claim
52 words
An automated cytological specimen classifier, comprising a low resolution neural network for performing primary classification of a cytological specimen, a high resolution neural network for performing secondary classification, and means for coupling said low and high resolution neural networks to pass data representing locations of interest from the former to the latter.
Claim 11
Independent Claim
31 words
A method of classifying cytological specimens, comprising primarily classifying a specimen using a first classifier to determine locations of interest and secondarily classifying such locations of interest using a neural network.
Claim 12
Independent Claim
31 words
A method of classifying cytological specimens, comprising primarily classifying a specimen using a first classifier to determine locations of interest and secondarily classifying such locations of interest using a neural network.
Claim 13
Dependent Claim
37 words
The method of claim 9, wherein said primarily classifying comprises using a video camera or charge coupled device (CCD) to obtain images of the specimen, a digitizer to digitize such images and an integrated optical density detector.
Claim 14
Dependent Claim
14 words
The method of claim 9, wherein said primary classifying comprises using a neural network.
References Cited (Backward Citations)
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