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

A method for computer-aided detection of possible anomalies in a digitized image comprising a plurality of M.times.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.times.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.times.q picture elements, where p<m and q<n;
• d) normalizing the image data from the p.times.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.