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 CLAHEsettings 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 CLAHEsettings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved. Copyright © 1998 by W.S. Saunders Company KEY WORDS: mammography, image processing, contrast limited adaptive histogram equalization, observer studies, breast cancer, spiculations."PPROXIMATELY 10% to 15% of palpable ft malignancies are not visible mammographically.1 It is highly likely that many nonpalpable cancers are also not visible with current technology. Digital mammography might allow for greater contrast and improved detection of small and early tumors over standard film screen technology, especially if image processing is used to improve image contrast.2-5We have previously published two articles reporting laboratory results that show improved performance by students in finding simulated masses and simulated clustered calcifications embedded in dense mammographic background when Intensity Win-

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