Comparing the performance of mammographic enhancement algorithms: a preference study

Abstract

Objective: The objective of this study was to compare the performance of four image enhancement algorithms on secondarily digitized (i.e., digitized from film) mammograms containing masses and microcalcifications of known pathology in a clinical soft-copy display setting.

Materials and methods: Four different image processing algorithms (adaptive unsharp masking, contrast-limited adaptive histogram equalization, adaptive neighborhood contrast enhancement, and wavelet-based enhancement) were applied to one image of secondarily digitized mammograms of forty cases (10 each of benign and malignant masses and 10 each of benign and malignant microcalcifications). The four enhanced images and the one unenhanced image were displayed randomly across three high-resolution monitors. Four expert mammographers ranked the unenhanced and the four enhanced images from 1 (best) to 5 (worst).

Results: For microcalcifications, the adaptive neighborhood contrast enhancement algorithm was the most preferred in 49% of the interpretations, the wavelet-based enhancement in 28%, and the unenhanced image in 13%. For masses, the unenhanced image was the most preferred in 58% of cases, followed by the unsharp masking algorithm (28%).

Conclusion: Appropriate image enhancement improves the visibility of microcalcifications. Among the different algorithms, the adaptive neighborhood contrast enhancement algorithm was preferred most often. For masses, no significant improvement was observed with any of these image processing approaches compared with the unenhanced image. Different image processing approaches may need to be used, depending on the type of lesion. This study has implications for the practice of digital mammography.