Name
Title | ||
|---|---|---|
Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms |
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. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BF03178082 | J. Digital Imaging |
Keywords | Field | DocType |
mammography,image processing,contrast limited adaptive histogram equalization,observer studies,breast cancer,spiculations | Computer vision,Mammography,Computer science,Image processing,Adaptive histogram equalization,Pixel,Artificial intelligence,Radiographic Image Enhancement | Journal |
Volume | Issue | ISSN |
11 | 4 | 0897-1889 |
Citations | PageRank | References |
88 | 3.77 | 3 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Etta D. Pisano | 1 | 344 | 49.06 |
| Shuquan Zong | 2 | 88 | 3.77 |
| Bradley M. Hemminger | 3 | 456 | 38.24 |
| Marla DeLuca | 4 | 91 | 5.55 |
| R. Eugene Johnston | 5 | 118 | 15.87 |
| Keith Muller | 6 | 158 | 13.40 |
| M. Patricia Braeuning | 7 | 93 | 7.04 |
| Stephen M. Pizer | 8 | 2000 | 262.21 |