Featureless, knowledge-based CAD systems are an attractive alternative to feature-based CAD because they require no
to minimal image preprocessing. Such systems compare images directly using the raw image pixel values rather than
relying on low-level image features. Specifically, information-theoretic (IT) measures such as mutual information (MI)
have been shown to be an effective, featureless, similarity measure for image comparisons. MI captures the statistical
relationship between the gray level values of corresponding image pixels. In a CAD system developed at our laboratory,
the above concept has been applied for location-specific detection of mammographic masses. The system is designed to
operate on a fixed size region of interest (ROI) extracted around a suspicious mammographic location. Since mass sizes
vary substantially, there is a potential drawback. When two ROIs are compared, it is unclear how much the parenchymal
background contributes in the calculated MI. This uncertainty could deteriorate CAD performance in the extreme cases,
namely when a small mass is present in the ROI or when a large mass extends beyond the fixed size ROI. The present
study evaluates the effect of ROI size on the overall CAD performance and proposes multisize analysis for possible
improvement. Based on two datasets of ROIs extracted from DDSM mammograms, there was a statistically significant
decline of the CAD performance as the ROI size increased. The best size ranged between 512x512 and 256x256 pixels.
Multisize fusion analysis using a linear model achieved further improvement in CAD performance for both datasets.