Computer aided detection of masses in mammography using subregion Hotelling observers

Authors

  • Baydush Alan H.,

    1. Department of Radiation Oncology, Physics Division, Duke University Medical Center, Durham, North Carolina 27710
    2. Department of Biomedical Engineering, Duke University, Durham, North Carolina 27710
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  • Catarious David M.,

    1. Department of Biomedical Engineering, Duke University, Durham, North Carolina 27710
    2. Digital Imaging Research Division, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710
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  • Abbey Craig K.,

    1. Department of Biomedical Engineering, University of California, Davis, California 95616
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  • Floyd Carey E.

    1. Department of Biomedical Engineering, Duke University, Durham, North Carolina 27710
    2. Digital Imaging Research Division, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710
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Abstract

We 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. Each ROI was extracted at a size of 1024×1024 pixels and subsampled to 128×128 pixels. For the detection task, cancer and benign cases were considered positive and normal was considered negative. All positive cases had the lesion centered in the ROI. We chose to investigate the subregion Hotelling observer as a classifier to detect masses. The Hotelling observer incorporates information about the signal, the background, and the noise correlation for prediction of positive and negative and is the optimal detector when these are known. For our study, 225 subregion Hotelling observers were set up in a 15×15 grid across the center of the ROIs. Each separate observer was designed to “observe,” or discriminate, an 8×8 pixel area of the image. A leave one out training and testing methodology was used to generate 225 “features,” where each feature is the output of the individual observers. The 225 features derived from separate Hotelling observers were then narrowed down by using forward searching linear discriminants (LDs). The reduced set of features was then analyzed using an additional LD with receiver operating characteristic (ROC) analysis. The 225 Hotelling observer features were searched by the forward searching LD, which selected a subset of 37 features. This subset of 37 features was then analyzed using an additional LD, which gave a ROC area under the curve of 0.9412+/0.006 and a partial area of 0.6728. Additionally, at 98% sensitivity the overall classifier had a specificity of 55.9% and a positive predictive value of 69.3%. Preliminary results suggest that using subregion Hotelling observers in combination with LDs can provide a strong backbone for a CAD scheme to help radiologists with detection. Such a system could be used in conjunction with CAD systems for false positive reduction.

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