Evaluation of Stellate Lesion Detection in a Standard Mammogram Data Set

We have previously reported on a method for the automatic detection of stellate lesions in digitized mammograms, and on our tests of that method on image data with known diagnoses. This earlier investigation was based on a limited set of 10 test images, each with a stellate lesion. As our approach is one of supervised training, half of the data was used as a training set, and so the performance results were necessarily coarse. Accordingly there is value in testing these algorithms on a larger data set that will not only provide more lesions but also truly undiseased tissue. A new mammogram data set addresses both of these concerns, as it contains examples of twelve stellate lesions, as well as fifty examples of entirely normal mammograms. Further, as this data set has been made widely available to all interested researchers, performance results for specific algorithms on this data set are of particular value, as they can be directly compared to the performance of other algorithms similarly applied. Thus the main contribution of the current paper is to exhaustively evaluate the performance of this stellate lesion detection algorithm on the new mammogram data set. A secondary aim is to present a revision of the spatial integration step which generates the final report of a lesion’s existence, one that facilitates the extraction of ROC performance statistics.