Detection of simulated lesions on data-compressed digital mammograms.
OBJECTIVE
To evaluate retrospectively the effect of a wavelet-based compression method on the detection of simulated masses of various sizes and clustered microcalcifications on data-compressed digital mammograms.


METHODS
The images used in this study were acquired with institutional review board approval and patient informed consent, both of which allowed subsequent image data analysis. Patient identification was removed from images, and the study complied with requirements of the Health Insurance Portability and Accountability Act. Masses 3, 6, and 8 mm in diameter were analytically simulated and added to clinical mammographic backgrounds. In addition, microcalcifications were extracted from a clinical mammogram and hybridized with simulated microcalcifications for use in this study. Image compression conditions of 1:1, 15:1, and 30:1 were investigated. Observer responses were recorded with a six-point rating scale, and receiver operating characteristic (ROC) analysis was performed. In addition, two well-established numeric observer models were used to study the effect of image compression under the same compression conditions as were used with human observers. Analysis of variance was performed after observer adjustment to compare the mean values for area under the ROC curve (A(z)) across the three compression levels for the masses and microcalcification clusters.


RESULTS
The results of the study indicated no significant differences in the A(z) values for masses with the compression conditions investigated. For images of microcalcifications, there were significant differences in A(z) values between compression ratios of 1:1 and 30:1 (P = .0005) and of 15:1 and 30:1 (P = .004); the difference between compression ratios of 1:1 and 15:1 was nonsignificant (P = .053). The observer models and human observers exhibited similar trends in detection of the masses investigated in this study.


CONCLUSIONS
Detection of simulated masses was not affected by the compression method with the conditions used in this study, while the detection of microcalcifications was significantly reduced with a compression ratio of more than 15:1.
Suryanarayanan
Sankararaman
S
1 Department of Radiology, Emory University School of Medicine, Winship Cancer Institute, 1701 Uppergate Dr, Bldg C, Suite 5018, Atlanta, GA 30322, USA.
Karellas
Andrew
A
Vedantham
Srinivasan
S
Waldrop
Sandra M
SM
D'Orsi
Carl J
CJ
D000704:Analysis of Variance; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D014584:User-Computer Interface