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Mutual information is a popular intensity-based image similarity measure mainly used in image registration. This measure has been also very successful as the similarity metric in our knowledge-based computer-assisted detection (CADe) system for the detection of masses in screening mammograms. Our CADe system is designed to assess a new, query case based on its similarity with known cases stored in the knowledge database. However, intensity-based mutual information captures only relationships between the gray level values of corresponding pixels. This study presents a novel advancement of our CADe system by incorporating neighborhood textural information when estimating the mutual information of two images. Specifically, an entropy filter is applied to the images, effectively replacing each image pixel value with its neighborhood entropy. This pixel-based entropy is a localized measure of image texture. Then, the information-theoretic CAD system is asked to make a decision regarding the query case using the texture-based mutual information similarity metric. The entropy-based image enhancement and MI-based decision making processes are repeated at different neighborhood scales. Finally, an artificial network merges intensity-based and texture-based decisions to investigate possible improvements in mass detection performance. Given a database of 1,820 regions of interest (ROIs) extracted from screening mammograms (901 depicting a biopsy-proven mass and 919 depicting normal parenchyma) and a leave-one out sampling scheme, the study showed that our CADe system achieves an ROC area of 0.87±0.01 using the intensity-based ROC. The ROC performance for the texture-based CADe system ranges from 0.69±0.01 to 0.83±0.01 depending on the scale of analysis. The synergistic approach of the ANN using both intensity-based and texture-based information resulted in statistically significantly better performance with an ROC area index of 0.93±0.01.
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