Publications by authors named "D M Catarious"
Previously, we developed a simple Laguerre-Gauss (LG) channelized Hotelling observer (CHO) for incorporation into our mass computer-aided detection (CAD) system. This LG-CHO was trained using initial detection suspicious region data and was empirically optimized for free parameters. For the study presented in this paper, we wish to create a more optimal mass detection observer based on a novel combination of LG channels.
View Article and Find Full Text PDFIn this article, we present a characterization of the effect of difference of Gaussians (DoG) filters in the detection of mammographic regions. DoG filters have been used previously in mammographic mass computer-aided detection (CAD) systems. As DoG filters are constructed from the subtraction of two bivariate Gaussian distributions, they require the specification of three parameters: the size of the filter template and the standard deviations of the constituent Gaussians.
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- A new computer-aided detection (CAD) system for identifying masses in mammograms has been developed, improving on a previous version that used a basic and imprecise localization method.
- A more robust segmentation routine was introduced to better define the morphology of the masses, enhancing the CAD system’s ability to differentiate these from other structures in mammograms.
- The new algorithm, tested on 183 mammographic images, iteratively refines mass boundaries and employs ROC analysis to evaluate its performance, demonstrating improved accuracy in identifying both benign and malignant masses.
The purpose of this study was to develop a knowledge-based scheme for the detection of masses on digitized screening mammograms. The computer-assisted detection (CAD) scheme utilizes a knowledge databank of mammographic regions of interest (ROIs) with known ground truth. Each ROI in the databank serves as a template.
View Article and Find Full Text PDFWe propose to investigate the use of the subregion Hotelling observer for the basis of a computer aided detection scheme for masses in mammography. A database of 1320 regions of interest (ROIs) was selected from the DDSM database collected by the University of South Florida using the Lumisys scanner cases. The breakdown of the cases was as follows: 656 normal ROIs, 307 benign ROIs, and 357 cancer ROIs.
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