December 2002
Volume 2, Issue 10
OSA Fall Vision Meeting Abstract  |   December 2002
Classification images for the detection of a simulated mass in mammographic images
Author Affiliations
  • Craig Abbey
    Biomedical Engineering, U. C. Davis, Davis, CA, USA
  • Steve Shimozaki
    Department of Psychology, University of California, Santa Barbara, Santa Barbara, USA
  • Alan Baydush
    Duke University, Durham, NC, USA
  • David Catarious
    Biomedical Engineering, Duke University, Durham, NC, USA
  • Carey Floyd
    Radiology, Duke University, Durham, NC, USA
  • Miguel P. Eckstein
    Department of Psychology, UC Santa Barbara, Santa Barbara, CA, USA
Journal of Vision December 2002, Vol.2, 124. doi:
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      Craig Abbey, Steve Shimozaki, Alan Baydush, David Catarious, Carey Floyd, Miguel P. Eckstein; Classification images for the detection of a simulated mass in mammographic images. Journal of Vision 2002;2(10):124.

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      © ARVO (1962-2015); The Authors (2016-present)

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Mammography is a widely used diagnostic tool for early detection of breast cancer, and visual detection of lesions in a mammogram is a very common visual task in radiology departments. There is significant interest in improving diagnostic performance in mammography through image processing and computer-aided diagnosis. However, it is still not clear at a basic computational level how an observer detects a low contrast lesion that is masked by the presence of normal anatomical structures and quantum noise picked up during image acquisition process.


As a first step in examining this question, we use a probit-regression approach to obtain maximum-likelihood estimates of a linear classification image for the task of detecting a low-contrast simulated lesion (5 mm diameter) that has been embedded in mammographic backgrounds derived from a database of digitized mammograms. A total of five subjects participated in 2AFC detection experiments to obtain psychophysical decision data from human observers. We estimate the classification images using probit regression for a set of features that are defined by radial-frequency bands in Discrete Fourier-Transform (DFT) domain. These templates are compared to the templates of a number of linear filter models. Our estimated templates for detecting the mass contain a band of heavily weighted spatial frequencies from 0.08 to 0.3 cycles/mm.

Abbey, C., Shimozaki, S., Baydush, A., Catarious, D., Floyd, C., Eckstein, M. P.(2002). Classification images for the detection of a simulated mass in mammographic images [Abstract]. Journal of Vision, 2( 10): 124, 124a,, doi:10.1167/2.10.124. [CrossRef]

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