Proceedings Article | 17 March 2008
Proc. SPIE. 6915, Medical Imaging 2008: Computer-Aided Diagnosis
KEYWORDS: Breast, Statistical analysis, Computer aided diagnosis and therapy, Tissues, Image segmentation, Diagnostics, Image analysis, Medical imaging, Mammography, CAD systems
Identifying the corresponding image pair of a lesion is an essential
step for combining information from different views of the lesion
to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images
from different views of a lesion from non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index(RGI) based segmentation and an active contour model, was initially
applied to extract mass lesions from the surrounding tissues. Then
various lesion features were automatically extracted from each of
the two views of each lesion to quantify the characteristics of margin,
shape, size, texture and context of the lesion, as well as its distance
to nipple. We employed a two-step method to select an effective subset
of features, and combined it with a BANN to obtain a discriminant
score, which yielded an estimate of the probability that the two images
are of the same physical lesion. ROC analysis was used to evaluate
the performance of the individual features and the selected feature
subset in the task of distinguishing between corresponding and non-corresponding
pairs. By using a FFDM database with 124 corresponding image pairs
and 35 non-corresponding pairs, the distance feature yielded an AUC
(area under the ROC curve) of 0.8 with leave-one-out evaluation
by lesion, and the feature subset, which includes distance feature,
lesion size and lesion contrast, yielded an AUC of 0.86. The improvement
by using multiple features was statistically significant as compared
to single feature performance. (p<0.001)
