Image processing has a significant impact on the detection of calcification clusters in digital mammography, according to an article published in the American Journal of Roentgenology.
Researchers from the United Kingdom, United States and Belgium performed a retrospective observer study to investigate the effect of image processing on the detection of cancers using digital mammography images.
The researchers collected 270 pairs of breast images, one view each of both breasts, from 238 patients with pathology-proven breast cancer; 185 women had presented for screening and 53 women presented with symptoms of cancer. Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy.
The breast images were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement) and pseudo–film-screen (no enhancement).
|80 image pairs||Subtle malignant masses|
|30 image pairs||Biopsy-proven benign lesions|
|80 image pairs||Simulated calcification clusters|
An expert radiologist selected 80 cases with malignant noncalcification lesions, such as mass, architectural distortion or asymmetric density. These cases had lesion conspicuity that was subtle or very subtle but still detectable. A total of 83 noncalcifications were detected. One patient had bilateral cancer with noncalcification cancers in both breasts.
The images of 52 other patients with biopsy-proven benign lesions were also inspected and the lesion locations were marked. Thirty patients from this group of 52 were randomly selected for the study. Their images showed a mixture of calcification and noncalcification lesions with a mixture of conspicuities.
The results showed that 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 authors wrote. “The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40).”
The researchers concluded that the standard image processing showed the best results when looking for calcification clusters and they suggest that the effect on cancer detection should be considered when selecting the type of image processing in the future.