Correlative feature analysis on FFDM

Abstract

Identifying the corresponding images of a lesion in different views is an essential step in improving the diagnostic ability of both radiologists and computer-aided diagnosis (CAD) systems. Because of the nonrigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this pilot study, we present a computerized framework that differentiates between corresponding images of the same lesion in different views and noncorresponding images, i.e., images of different lesions. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, is applied to extract mass lesions from the surrounding parenchyma. Then various lesion features are automatically extracted from each of the two views of each lesion to quantify the characteristics of density, size, texture and the neighborhood of the lesion, as well as its distance to the nipple. A two-step scheme is employed to estimate the probability that the two lesion images from different mammographic views are of the same physical lesion. In the first step, a correspondence metric for each pairwise feature is estimated by a Bayesian artificial neural network (BANN). Then, these pairwise correspondence metrics are combined using another BANN to yield an overall probability of correspondence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing corresponding pairs from noncorresponding pairs. Using a FFDM database with 123 corresponding image pairs and 82 noncorresponding pairs, the distance feature yielded an area under the ROC curve (AUC) of 0.81 +/- 0.02 with leave-one-out (by physical lesion) evaluation, and the feature metric subset, which included distance, gradient texture, and ROI-based correlation, yielded an AUC of 0.87 +/- 0.02. The improvement by using multiple feature metrics was statistically significant compared to single feature performance.

Figure 1

Schematic diagram of the proposed correlative feature analysis.

Figure 2

An example of two lesions in the same breast as seen in CC view (left) and ML view (right). The arrow indicates the correspondence of the same physical lesion in different views.

Figure 3

Lesion neighborhood illustration.

Figure 4

The correlation between distance features of the same lesions in CC and ML views. The distance feature is defined as the Euclidean distance between the nipple location and the mass center of the lesion. Here, the nipple location is manually identified.

Figure 5

Two examples of nipple markers. Nipple markers are bright spots close to the breast skin boundary, as indicated by arrows.

Figure 6

(a) The scatter plots of three features (distance, diameter, and texture) generated from lesions seen on CC and ML views. (b) The distribution of the output correspondence metrics of these features obtained from the first BANN stage.

Figure 7

Segmentation results for a benign lesion and a malignant lesion. The solid lines in the upper four images depict the lesion margin as outlined by a radiologist, and the solid lines in the bottom four images are segmentation results from our previously-reported automatic dual-stage method (Ref. 20). (a) CC view of a benign lesion, (b) the corresponding ML view of the benign lesion, (c) CC view of a malignant lesion, and (d) the corresponding ML view of the malignant lesion.

Figure 8

The correlation between distance features calculated from manually identified nipple locations and those calculated from computer-identified nipple locations.

Figure 9

Correlation coefficients between CC and ML views and associated 95% confidence intervals of the 18 features extracted from benign (solid) and malignant (dash) lesions. All the lesions were segmented via an automatic segmentation algorithm. Left: corresponding image pairs. Right: noncorresponding image pairs.