Interpolation algorithms for digital mammography systems with multiple detectors

In some full-field digital mammography systems, multiple detectors are abutted together, and the physical gaps between adjacent detectors produce seams between the resultant subimages. In this study, a variety of interpolation algorithms for estimating the missing information in the seams were compared, and their effect on image quality was evaluated.

Eight representative interpolation algorithms were selected, including nearest neighbor, one-dimensional and two-dimensional weighting, mean value, one-dimensional and two-dimensional polynomial, and one-dimensional and two-dimensional cubic spline interpolation methods. These methods were applied to digital mammograms and phantom images. The effectiveness of each algorithm was evaluated for accuracy and geometric distortion.

These interpolation algorithms offered similar accuracy in estimating missing image information. The weighting, polynomial, and cubic spline interpolation algorithms introduced less geometric distortion than the nearest neighbor and mean value interpolation algorithms. All algorithms were more effective in estimating larger, lower-contrast features (such as breast masses) than in estimating smaller, higher-contrast features (such as breast microcalcifications). Small microcalcifications within the seams cannot be recovered with interpolation. The probability of a microcalcification in a seam is small, however, and the failure to image a few microcalcifications of a cluster generally does not substantially alter diagnostic performance.

In the development of full-field digital breast imaging systems, appropriate interpolation algorithms can satisfactorily fill in narrow gaps between adjacent detectors. The one-dimensional weighting interpolation method seems an effective and efficient choice.