Computer-aided detection of clustered microcalcifications on full-field digital mammograms: A two-view information fusion scheme for FP reduction

Abstract

We are developing new techniques to improve the performance of our computer-aided detection (CAD) system for clustered microcalcifications on full-field digital mammograms (FFDMs). In this study, we designed an information fusion scheme by using 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 geometrical information. Candidate pairs were classified as true and false pairs 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 192 FFDM images was collected from 96 patients at the University of Michigan. All patients had two mammographic views. This data set contained 96 microcalcification clusters, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. For training and testing the classifiers, the data set was partitioned into two independent subsets with the malignant cases equally distributed to the two subsets. One subset was used for training and the other subset was used for testing. We compared three computerized methods for geometrically pairing cluster candidates on two mammographic views. The areas under the fitted ROC curves were 0.75±0.01, 0.74±0.01, and 0.76±0.01 for the three methods, respectively. The difference between any two methods measured by the area under the fitted ROC curve, A z , was not statistically significant (p > 0.05). We also evaluated a new hybrid pairing scheme that used two different sensitivity levels for defining cluster pairs based on the single-view scores. The single-view CAD system achieved cluster-based sensitivities of 75%, 80%, and 85% at 0.48, 0.86, and 1.05 FPs/image, respectively. The joint two-view CAD system achieved the same sensitivity levels at 0.29, 0.46, and 0.89 FPs/image. When the hybrid pairing was used in the joint two-view CAD system, the same cluster-based sensitivities were achieved at 0.26, 0.37, and 0.88 FPs/image. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters.