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3 March 2009The use of contextual information for computer aided
detection of masses in mammograms
In breast cancer screening, radiologists not only look at local properties of suspicious regions in the mammogram
but take also into account more general contextual information. In this study we investigated the use of similar
information for computer aided detection of malignant masses. We developed a new set of features that combine
information from the candidate mass region and the whole image or mammogram. The developed context
features were constructed to give information about suspiciousness of a region relative to other areas in the
mammogram, the location in the image, the location in relation to dense tissue and the overall amount of dense
tissue in the mammogram. We used a step-wise floating feature selection algorithm to select subsets from the
set of available features. Feature selection was performed two times, once using the complete feature set (37
context and 40 local features) and once using only the local features. It was found that in the subsets selected
from the complete feature set 30-60% were context features. At most one local feature present in the subset
containing context features was not present in the subset without context features. We validated the performance
of the selected subsets on a separate data set using cross validation and bootstrapping. For each subset size we
compared the performance obtained using the features selected from the complete feature set to the performance
obtained using the features selected from the local feature set. We found that subsets containing context features
performed significantly better than feature sets containing no context features.
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Rianne Hupse, Nico Karssemeijer, "The use of contextual information for computer aided detection of masses in mammograms," Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600Q (3 March 2009); https://doi.org/10.1117/12.812233