Journal of Digital Imaging

, 11:193

Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

  • Etta D. Pisano
  • Shuquan Zong
  • Bradley M. Hemminger
  • Marla DeLuca
  • R. Eugene Johnston
  • Keith Muller
  • M. Patricia Braeuning
  • Stephen M. Pizer
Article

DOI: 10.1007/BF03178082

Cite this article as:
Pisano, E.D., Zong, S., Hemminger, B.M. et al. J Digit Imaging (1998) 11: 193. doi:10.1007/BF03178082

Abstract

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.

Key Words

mammographyimage processingcontrast limited adaptive histogram equalizationobserver studiesbreast cancerspiculations

Copyright information

© Society for Imaging Informatics in Medicine 1998

Authors and Affiliations

  • Etta D. Pisano
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • Shuquan Zong
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • Bradley M. Hemminger
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • Marla DeLuca
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • R. Eugene Johnston
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • Keith Muller
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • M. Patricia Braeuning
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  • Stephen M. Pizer
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
  1. 1.Department of RadiologyThe University of North CarolinaChapel HillUSA
  2. 2.Department of Computer ScienceThe University of North CarolinaChapel HillUSA
  3. 3.Department of Biomedical EngineeringThe University of North CarolinaChapel HillUSA
  4. 4.Department of BiostatisticsThe University of North CarolinaChapel HillUSA
  5. 5.School of Public Health and College of Arts and Sciencesthe UNC School of MedicineUSA
  6. 6.UNC-Lineberger Comprehensive Cancer CenterUSA