Automatic image analysis and spot classification for detection of pathogenic Escherichia coli on glass slide DNA microarrays

A computer algorithm was created to analyze and quantify scanned images from DNA microarray slides developed for detecting pathogenic Escherichia coli isolates recovered from agricultural food products. The algorithm computed centroid locations for signal and background pixel intensities in RGB space and defined a plane perpendicular to the line connecting the centroids as a decision boundary. The algorithm was tested on 1534 potential spot locations which were visually classified depending on the strength of the signal. Three other standard measures of SNR (SSR, SBR, and SSDR) were also performed for each potential spot location. The number of errors as compared to visual classifications was computed for each of the four measures. SSR and SSDR, which depend on pixel intensity standard deviations, performed poorly with high false positive results, while the current algorithm and SBR, which were independent of standard deviations, performed much better. Overall error rates were 1.4% for the reported algorithm, 2.0% for SBR, 14.2% for SSDR, and 16.8% for SSR.