Effects of different compression techniques on diagnostic accuracies of breast masses on digitized mammograms.
BACKGROUND
The JPEG 2000 compression technique has recently been introduced into the medical imaging field. It is critical to understand the effects of this technique on the detection of breast masses on digitized images by human observers.


OBJECTIVE
To evaluate whether lossless and lossy techniques affect the diagnostic results of malignant and benign breast masses on digitized mammograms.


METHODS
A total of 90 screen-film mammograms including craniocaudal and lateral views obtained from 45 patients were selected by two non-observing radiologists. Of these, 22 cases were benign lesions and 23 cases were malignant. The mammographic films were digitized by a laser film digitizer, and compressed to three levels (lossless and lossy 20:1 and 40:1) using the JPEG 2000 wavelet-based image compression algorithm. Four radiologists with 10-12 years' experience in mammography interpreted the original and compressed images. The time interval was 3 weeks for each reading session. A five-point malignancy scale was used, with a score of 1 corresponding to definitely not a malignant mass, a score of 2 referring to not a malignant mass, a score of 3 meaning possibly a malignant mass, a score of 4 being probably a malignant mass, and a score of 5 interpreted as definitely a malignant mass. The radiologists' performance was evaluated using receiver operating characteristic analysis.


RESULTS
The average Az values for all radiologists decreased from 0.8933 for the original uncompressed images to 0.8299 for the images compressed at 40:1. This difference was not statistically significant. The detection accuracy of the original images was better than that of the compressed images, and the Az values decreased with increasing compression ratio.


CONCLUSIONS
Digitized mammograms compressed at 40:1 could be used to substitute original images in the diagnosis of breast cancer.
Liang
Zhigang
Z
Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Du
Xiangying
X
Liu
Jiabin
J
Yang
Yanhui
Y
Rong
Dongdong
D
Yao
Xinyu
X
Li
Kuncheng
K
D000368:Aged; D000704:Analysis of Variance; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D044962:Data Compression / Q000379:methods*; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D012372:ROC Curve