Joint two-view information for computerized detection of microcalcifications on mammograms.
We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Candidate pairs were classified with a similarity classifier that used the joint information from both views. Each cluster candidate was also characterized by its single-view features. The outputs of the similarity classifier and the single-view classifier were fused and the cluster candidate was classified as a true microcalcification cluster or a false-positive (FP) using the fused two-view information. A data set of 116 pairs of mammograms containing microcalcification clusters and 203 pairs of normal images from the University of South Florida (USF) public database was used for training the two-view detection algorithm. The trained method was tested on an independent test set of 167 pairs of mammograms, which contained 71 normal pairs and 96 pairs with microcalcification clusters collected at the University of Michigan (UM). The similarity classifier had a very low FP rate for the test set at low and medium levels of sensitivity. However, the highest mammogram-based sensitivity that could be reached by the similarity classifier was 69%. The single-view classifier had a higher FP rate compared to the similarity classifier, but it could reach a maximum mammogram-based sensitivity of 93%. The fusion method combined the scores of these two classifiers so that the number of FPs was substantially reduced at relatively low and medium sensitivities, and a relatively high maximum sensitivity was maintained. For the malignant microcalcification clusters, at a mammogram-based sensitivity of 80%, the FP rates were 0.18 and 0.35 with the two-view fusion and single-view detection methods, respectively. When the training and test sets were switched, a similar improvement was obtained, except that both the fusion and single-view detection methods had superior test performances on the USF data set than those on the UM data set. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters.
Sahiner
Berkman
B
Department of Radiology, University of Michigan, Ann Arbor 48109-0904, USA. berki@umich.edu
Chan
Heang-Ping
HP
Hadjiiski
Lubomir M
LM
Helvie
Mark A
MA
Paramagul
Chinatana
C
Ge
Jun
J
Wei
Jun
J
Zhou
Chuan
C
D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000175:diagnosis*; D002114:Calcinosis / Q000175:diagnosis*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software