Journal article
Improving the Detection of Simulated Masses in Mammograms through Two Different Image-Processing Techniques
Academic radiology, Vol.8(9), pp.845-855
2001
DOI: 10.1016/S1076-6332(03)80762-6
PMID: 11724039
Abstract
The purpose of this study was to determine whether contrast-limited adaptive histogram equalization (CLAHE) or histogram-based intensity windowing (HIW) improves the detection of simulated masses in dense mammograms.
Simulated masses were embedded in portions of mammograms of patients with dense breasts; the mammograms were digitized at 50 μm per pixel, 12 bits deep. In two different experiments, images were printed both with no processing applied and with related parameter settings of two image-processing methods. A simulated mass was embedded in a realistic background of dense breast tissue, with its position varied. The key variables in each trial included the position of the mass, the contrast levels of the mass relative to the background, and the selected parameter settings for the image-processing method.
The success in detecting simulated masses on mammograms with dense backgrounds depended on the parameter settings of the algorithms used. The best HIW setting performed better than the best fixed-intensity window setting and better than no processing. Performance with the best CLAHE settings was no different from that with no processing. In the HIW experiment, there were no significant differences in observer performance between processing conditions for radiologists and nonradiologists.
HIW should be tested in clinical images to determine whether the detection of masses by radiologists can be improved. CLAHE processing will probably not improve the detection of masses on clinical mammograms.
Details
- Title: Subtitle
- Improving the Detection of Simulated Masses in Mammograms through Two Different Image-Processing Techniques
- Creators
- Bradley M Hemminger - Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7510, USAShuquan Zong - Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599-7510, USAKeith E Muller - Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7510, USAChristopher S Coffey - Department of Preventive Medicine, Division of Biostatistics, Vanderbilt University, Nashville, Tenn, USAMarla C DeLuca - Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7510, USAR.Eugene Johnston - Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7510, USAEtta D Pisano - Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7510, USA
- Resource Type
- Journal article
- Publication Details
- Academic radiology, Vol.8(9), pp.845-855
- Publisher
- Elsevier Inc
- DOI
- 10.1016/S1076-6332(03)80762-6
- PMID
- 11724039
- ISSN
- 1076-6332
- eISSN
- 1878-4046
- Language
- English
- Date published
- 2001
- Academic Unit
- Biostatistics
- Record Identifier
- 9984214702302771
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