In mammography, computer-aided diagnosis (CAD) techniques for mass detection and classification mainly use local image information to determine whether a region is abnormal or not. There is a lot of interest in developing CAD methods that use context, asymmetry, and multiple view information. However, it is not clear to what extent this may improve CAD results. In this study, we made use of human observers to investigate the potential benefit of using context information for CAD. We investigated to what extent human readers make use of context information derived from the whole breast area and from asymmetry for the tasks of mass detection and classification. Results showed that context information can be used to improve CAD programs for mass detection. However, there is still a lot to be gained from improvement of local feature extraction and classification. This is demonstrated by the fact that the observers did much better in classifying true positive (TP) and false positive (FP) regions than the CAD program. For classification of benign and malignant masses context seems to be less important.
For radiologists lesion margin appearance is of high importance when
classifying breast masses as malignant or benign lesions. In this
study, we developed different measures to characterize the margin of a
lesion. Towards this goal, we developed a series of algorithms to
quantify the degree of sharpness and lobulation of a mass
margin. Besides, to estimate spiculation of a margin, features
previously developed for mass detection were used. Images selected
from the publicly available data set "Digital Database for Screening
Mammography" were used for development and evaluation of these
algorithms. The data set consisted of 777 images corresponding to 382
patients. To extract lesions from the mammograms a segmentation
algorithm based on dynamic programming was used. Features were
extracted for each lesion. A k-nearest neighbor algorithm was used in
combination with a leave-one-out procedure to select the best features
for classification purposes. Classification accuracy was evaluated
using the area Az under the receiver operating characteristic curve. The average test Az value for the task of classifying masses on a single mammographic view was 0.79. In a case-based evaluation we obtained an Az value of 0.84.
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