title: The effect of image processing on the detection of cancers in digital mammography.
abstract: OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.
authors: 1 National Coordinating Centre for the Physics of Mammography, Royal Surrey County Hospital, NHS Foundation Trust, Guildford, GU2 7XX, UK.
authors: Lucy M
authors: LM
authors: Warren
authors: Rosalind M
authors: RM
authors: Given-Wilson
authors: Matthew G
authors: MG
authors: Wallis
authors: Julie
authors: J
authors: Cooke
authors: Mark D
authors: MD
authors: Halling-Brown
authors: Alistair
authors: A
authors: Mackenzie
authors: Dev P
authors: DP
authors: Chakraborty
authors: Hilde
authors: H
authors: Bosmans
authors: David R
authors: DR
authors: Dance
authors: Kenneth C
authors: KC
authors: Young
mesh_terms: D000368:Aged; D001706:Biopsy; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D006113:United Kingdom
keywords: digital mammography; image processing; observer performance