Image Processing Algorithms for Digital Mammography: A Pictorial Essay

Digital mammography systems allow manipulation of fine differences in image contrast by means of image processing algorithms. Different display algorithms have advantages and disadvantages for the specific tasks required in breast imaging—diagnosis and screening. Manual intensity windowing can produce digital mammograms very similar to standard screen-film mammograms but is limited by its operator dependence. Histogram-based intensity windowing improves the conspicuity of the lesion edge, but there is loss of detail outside the dense parts of the image. Mixture-model intensity windowing enhances the visibility of lesion borders against the fatty background, but the mixed parenchymal densities abutting the lesion may be lost. Contrast-limited adaptive histogram equalization can also provide subtle edge information but might degrade performance in the screening setting by enhancing the visibility of nuisance information. Unsharp masking enhances the sharpness of the borders of mass lesions, but this algorithm may make even an indistinct mass appear more circumscribed. Peripheral equalization displays lesion details well and preserves the peripheral information in the surrounding breast, but there may be flattening of image contrast in the nonperipheral portions of the image. Trex processing allows visualization of both lesion detail and breast edge information but reduces image contrast

Automated segmentation of regions of interest on hand radiographs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135137/1/mp7402.pd

Digitization requirements in mammography: Effects on computerâ aided detection of microcalcifications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134847/1/mp7354.pd

Computerâ aided diagnosis: Detection and characterization of hyperparathyroidism in digital hand radiographs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134793/1/mp6980.pd

Image compression in digital mammography: Effects on computerized detection of subtle microcalcifications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134782/1/mp7871.pd

Comparative Statistical Properties of Expected Utility and Area Under the ROC Curve for Laboratory Studies of Observer Performance in Screening Mammography

Rationale and objectivesOur objective is to determine whether expected utility (EU) and the area under the receiver operator characteristic (AUC) are consistent with one another as endpoints of observer performance studies in mammography. These two measures characterize receiver operator characteristic performance somewhat differently. We compare these two study endpoints at the level of individual reader effects, statistical inference, and components of variance across readers and cases.Materials and methodsWe reanalyze three previously published laboratory observer performance studies that investigate various x-ray breast imaging modalities using EU and AUC. The EU measure is based on recent estimates of relative utility for screening mammography.ResultsThe AUC and EU measures are correlated across readers for individual modalities (r = 0.93) and differences in modalities (r = 0.94 to 0.98). Statistical inference for modality effects based on multi-reader multi-case analysis is very similar, with significant results (P < .05) in exactly the same conditions. Power analyses show mixed results across studies, with a small increase in power on average for EU that corresponds to approximately a 7% reduction in the number of readers. Despite a large number of crossing receiver operator characteristic curves (59% of readers), modality effects only rarely have opposite signs for EU and AUC (6%).ConclusionsWe do not find any evidence of systematic differences between EU and AUC in screening mammography observer studies. Thus, when utility approaches are viable (i.e., an appropriate value of relative utility exists), practical effects such as statistical efficiency may be used to choose study endpoints