Application of neural networks as an aid in medical diagnosis and general anomaly detection

Claim 1

A method for computer-aided detection of possible anomalies in a digitized image comprising a plurality of M×N picture elements each representing an optical density in the digitized image, the method comprising the steps of: a) subdividing the digitized image into a plurality of predetermined regions each comprising m×n picture elements, where m<M and n<N; b) subtracting background from each predetermined region of the digitized image; c) selecting a subregion comprising p×q picture elements, where p<m and q<n; d) normalizing the image data from the p×q subregion; e) using a neural network system, analyzing each predetermined subregion of the digitized image to recognize any pattern indicative of an occurrence of a possible anomaly, the neural network system comprising at least two member neural networks each trained to recognize a particular predetermined anomaly type within a predetermined size range and to produce an output signal value indicative of the presence of said predetermined anomaly type; f) comparing each of the output values of each of the member neural networks to a first predetermined threshold value corresponding to each member neural network above which the presence of a possible anomaly is indicated; g) comparing each output value that exceeds each first predetermined threshold to each of the other output values that exceed each corresponding first predetermined threshold to select the maximum signal value; h) comparing the maximum signal value to a second predetermined threshold value above which the presence of a possible anomaly is indicated; i) based upon the comparisons of step h), determining the location within the digitized image of each possible anomaly; j) using a clustering analysis on the locations of possible anomalies of step i), identifying each vicinity on the digitized image at which a cluster of locations of possible anomalies occurs; and, k) creating a marker for each cluster, having a contour that surrounds all of the anomalies in each cluster, corresponding to the digitized location of each cluster.

Claim 2

A method for computer-aided detection of possible anomalies in a digitized image comprising a plurality of M×N picture elements each representing an optical density in the digitized image, the method comprising the steps of: a) subdividing the digitized image into a plurality of predetermined regions each comprising m×n picture elements, where m<M and n<N; b) subtracting background from each predetermined region of the digitized image; c) selecting a subregion comprising p×q picture elements, where p<m and q<n; d) normalizing the image data from the p×q subregion; e) using a neural network system, analyzing each predetermined subregion of the digitized image to recognize any pattern indicative of an occurrence of a possible anomaly, the neural network system comprising at least two member neural networks each trained to recognize a particular predetermined anomaly type within a predetermined size range and to produce an output signal value indicative of the presence of said predetermined anomaly type; f) comparing each of the output values of each of the member neural networks to a first predetermined threshold value corresponding to each member neural network above which the presence of a possible anomaly is indicated; g) comparing each output value that exceeds each first predetermined threshold to each of the other output values that exceed each corresponding first predetermined threshold to select the maximum signal value; h) comparing the maximum signal value to a second predetermined threshold value above which the presence of a possible anomaly is indicated; i) based upon the comparisons of step h), determining the location within the digitized image of each possible anomaly; j) using a clustering analysis on the locations of possible anomalies of step i), identifying each vicinity on the digitized image at which a cluster of locations of possible anomalies occurs; and, k) creating a marker for each cluster, having a contour that surrounds all of the anomalies in each cluster, corresponding to the digitized location of each cluster.