A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm
is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse
receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications,
detection sensitivity values of 0.75 and 0.81 are obtained
at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imag
