Bilateral analysis based false positive reduction for computer-aided mass detection

Abstract

We have developed a false positive (FP) reduction method based on analysis of bilateral mammograms for computerized mass detection systems. The mass candidates on each view were first detected by our unilateral computer-aided detection (CAD) system. For each detected object, a regional registration technique was used to define a region of interest (ROI) that is "symmetrical" to the object location on the contralateral mammogram. Texture features derived from the spatial gray level dependence matrices and morphological features were extracted from the ROI containing the detected object on a mammogram and its corresponding ROI on the contralateral mammogram. Bilateral features were then generated from corresponding pairs of unilateral features for each object. Two linear discriminant analysis (LDA) classifiers were trained from the unilateral and the bilateral feature spaces, respectively. Finally, the scores from the unilateral LDA classifier and the bilateral LDA asymmetry classifier were fused with a third LDA whose output score was used to distinguish true mass from FPs. A data set of 341 cases of bilateral two-view mammograms was used in this study, of which 276 cases with 552 bilateral pairs contained 110 malignant and 166 benign biopsy-proven masses and 65 cases with 130 bilateral pairs were normal. The mass data set was divided into two subsets for twofold cross-validation training and testing. The normal data set was used for estimation of FP rates. It was found that our bilateral CAD system achieved a case-based sensitivity of 70%, 80%, and 85% at average FP rates of 0.35, 0.75, and 0.95 FPs/image, respectively, on the test data sets with malignant masses. In comparison to the average FP rates for the unilateral CAD system of 0.58, 1.33, and 1.63, respectively, at the corresponding sensitivities, the FP rates were reduced by 40%, 44%, and 42% with the bilateral symmetry information. The improvement was statistically significance (p < 0.05) as estimated by JAFROC analysis.

Fig. 1

The characteristics of our mass data set: (a) distribution of mass sizes, (b) distribution of mass shapes, (c) distribution of mass margins, C: circumscribed, Ind: indistinct, M: microlobulated, Ob: obscured, Sp: spiculated, and (d) distribution of the breast density in terms of BI-RADS category estimated by a MQSA radiologist.

Fig. 2

Block diagram of the bilateral CAD system for mass detection on mammograms.

Fig. 3

An example of performing the mass candidate identification: (a) an original mammogram, (b) the detected breast boundary of (a), a mass is marked by the arrow, and (c) the detected mass candidates of (a).

Fig. 4

An example of obtaining the corresponding ROI of a mass candidate on the contralateral mammogram: (a) mass candidate on the left MLO view at m and (b) corresponding ROI on the right MLO view at m′.

Fig. 5

An example of obtaining the corresponding ROI based on the modified regional registration technique: (a) the nipple location (o), the shifted origin (n), and the mass candidate (m), and (b) corresponding ROI on the contralateral mammogram.

Fig. 6

(a) Mammogram containing a mass marked by the rectangular box. (b) A contralateral mammogram of (a) and the rectangular box is the corresponding ROI of the mass in (a) estimated by the automated regional registration technique. (c) ROI extracted from (a) containing a mass detected at the prescreening stage but excluded at the final stage of the unilateral CAD system. (d) The corresponding ROI in the contralateral breast. Bilateral analysis of this ROI pair increased the likelihood score of the mass which was then detected as a TP in the bilateral CAD system.

Fig. 7

(a) Mammogram and the rectangular ROI containing a mass candidate. (b) The contralateral mammogram of (a) and the rectangular box is the corresponding ROI of the mass candidate in (a). (c) ROI extracted from (a) containing normal tissue detected at the prescreening stage and included as a FP at the final stage of the unilateral CAD system. (d) The corresponding ROI in the contralateral breast. Bilateral analysis of this ROI pair reduced the likelihood score of the normal tissue which then became a TN in the bilateral CAD system.

Fig. 8

(a) Image-based and (b) case-based average test FROC curves from the unilateral and the bilateral CAD systems. The FP rates were estimated from detection on mammograms in the test subsets with masses.

Fig. 9

(a) Image-based and (b) case-based average test FROC curves from the unilateral and the bilateral CAD systems. The FP rates were estimated from detection on mammograms in the no-mass data set.

Fig. 10

(a) Image-based and (b) case-based average test FROC curves from the unilateral and bilateral CAD systems for detection on cases with malignant masses only. The FP rates were estimated from in the same data set.

Fig. 11

(a) Image-based and (b) case-based average test FROC curves from the unilateral and bilateral CAD systems for detection on cases with malignant masses only. The FP rates were estimated from the no-mass data set.