Share Email Print

Proceedings Paper

Effect of ROI size on the performance of an information-theoretic CAD system in mammography: multi-size fusion analysis
Author(s): Robert C. Ike III; Swatee Singh; Brian Harrawood; Georgia D. Tourassi
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691527 (17 March 2008); doi: 10.1117/12.772678
Show Author Affiliations
Robert C. Ike III, Duke Univ. Medical Ctr. (United States)
Swatee Singh, Duke Univ. Medical Ctr. (United States)
Brian Harrawood, Duke Univ. Medical Ctr. (United States)
Georgia D. Tourassi, Duke Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?