TY - GEN
T1 - Effect of ROI size on the performance of an information-theoretic CAD system in mammography
T2 - Medical Imaging 2008 - Computer-Aided Diagnosis
AU - Ike, Robert C.
AU - Singh, Swatee
AU - Harrawood, Brian
AU - Tourassi, Georgia D.
PY - 2008
Y1 - 2008
N2 - 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 512×512 and 256×256 pixels. Multisize fusion analysis using a linear model achieved further improvement in CAD performance for both datasets.
AB - 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 512×512 and 256×256 pixels. Multisize fusion analysis using a linear model achieved further improvement in CAD performance for both datasets.
KW - Classification and classifier design
KW - Detection
KW - Mammography
UR - http://www.scopus.com/inward/record.url?scp=44349167591&partnerID=8YFLogxK
U2 - 10.1117/12.772678
DO - 10.1117/12.772678
M3 - Conference contribution
AN - SCOPUS:44349167591
SN - 9780819470997
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2008 - Computer-Aided Diagnosis
Y2 - 19 February 2008 through 21 February 2008
ER -