High-performance wavelet compression for mammography: localization response operating characteristic evaluation.
OBJECTIVE
To evaluate the accuracy of a visually lossless, image-adaptive, wavelet-based compression method for achievement of high compression rates at mammography.


METHODS
The study was approved by the institutional review board of the University of South Florida as a research study with existing medical records and was exempt from individual patient consent requirements. Patient identifiers were obliterated from all images. The study was HIPAA compliant. An algorithm based on scale-specific quantization of biorthogonal wavelet coefficients was developed for the compression of digitized mammograms with high spatial and dynamic resolution. The method was applied to 500 normal and abnormal mammograms from 278 patients who were 32-85 years old, 85 of whom had biopsy-proved cancer. Film images were digitized with a charge-coupled device-based digitizer. The original and compressed reconstructed images were evaluated in a localization response operating characteristic experiment involving three radiologists with 2-10 years of experience in reading mammograms.


RESULTS
Compression rates in the range of 14:1 to 2051:1 were achieved, and the rates were dependent on the degree of parenchymal density and the type of breast structure. Ranges of the area under the receiver operating characteristic curve were 0.70-0.83 and 0.72-0.86 for original and compressed reconstructed mammograms, respectively. Ranges of the area under the localization response operating characteristic curve were 0.39-0.65 and 0.43-0.71 for original and compressed reconstructed mammograms, respectively. The localization accuracy increased an average of 6% (0.04 of 0.67) with the compressed mammograms. Localization performance differences were statistically significant with P = .05 and favored interpretation with the wavelet-compressed reconstructed images.


CONCLUSIONS
The tested wavelet-based compression method proved to be an accurate approach for digitized mammography and yielded visually lossless high-rate compression and improved tumor localization.
Kallergi
Maria
M
Department of Radiology, College of Medicine, H. Lee Moffitt Cancer Center & Research Institute, University of South Florida, Tampa, FL 33612-4799, USA. kallergi@moffitt.usf.edu
Lucier
Bradley J
BJ
Berman
Claudia G
CG
Hersh
Marla R
MR
Kim
Jihai J
JJ
Szabunio
Margaret S
MS
Clark
Robert A
RA
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*