Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis--local affine transformation for improved localization.
Analysis of interval change is important for mammographic interpretation. The aim of this study is to evaluate the use of an automated registration technique for computer-aided interval change analysis in mammography. Previously we developed a regional registration technique for identifying masses on temporal pairs of mammograms. In the current study, we improved lesion registration by including a local alignment step. Initially, the lesion position on the prior mammogram was estimated based on the breast geometry. An initial fan-shaped search region was then defined on the prior mammogram. In the second stage, the location of the fan-shaped region on the prior mammogram was refined by warping, based on an affine transformation and simplex optimization in a local region. In the third stage, a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of mammograms containing biopsyproven masses. Eighty-seven percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations and an average distance of 2.4 +/- 2.1 mm between their centroids. The average distance between the estimated and the true centroid of the lesions on the prior mammogram over all 124 temporal pairs was 4.2 +/- 5.7 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of mammograms. This improvement in accuracy resulted from the improved geometry estimation and the local affine transformation.
Hadjiiski
L
L
Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA.lhadjiiski@umich.edu
Chan
H P
HP
Sahiner
B
B
Petrick
N
N
Helvie
M A
MA
D055592:Biophysical Phenomena; D001703:Biophysics; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D017711:Nonlinear Dynamics; D011857:Radiographic Image Interpretation, Computer-Assisted; D013997:Time Factors