Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance.
Precise segmentation of microcalcifications is essential in the development of accurate mammographic computer-aided diagnosis (CAD) schemes. We have designed a radial gradient-based segmentation method for microcalcifications, and compared it to both the region-growing segmentation method currently used in our CAD scheme and to the watershed segmentation method. Two observer studies were conducted to subjectively evaluate the proposed segmentation method. The first study (A) required observers to rate the segmentation accuracy on a 100-point scale. The second observer evaluation (B) was a preference study in which observers selected their preferred method from three displayed segmentation methods. In study A, the observers gave an average accuracy rating of 88 for the radial gradient-based and 50 for the region-growing segmentation method. In study B, the two observers selected the proposed method 56% and 62% of the time. We also investigated the effect of the proposed segmentation method on the performance of computerized classification scheme in differentiating malignant from benign clustered microcalcifications. The performances of the classification scheme using a linear discriminant analysis (LDA) or a Bayesian artificial neural network classifier both showed statistically significant improvements when using the proposed segmentation method. The areas under the receiver-operating characteristic curves for case-based performance when using the LDA classifier were 0.86 with the proposed segmentation method, 0.80 with the region-growing method, and 0.83 with the watershed method.
Paquerault
Sophie
S
Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, USA. paquerau@uchicago.edu
Yarusso
Laura M
LM
Papaioannou
John
J
Jiang
Yulei
Y
Nishikawa
Robert M
RM
D000465:Algorithms; D001185:Artificial Intelligence; D001941:Breast Diseases / Q000145:classification / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000145:classification / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D008327:Mammography / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011230:Precancerous Conditions / Q000145:classification / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity