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Automated registration of diagnostic to prediagnostic x‐ray mammograms: Evaluation and comparison to radiologists’ accuracy
Author(s) -
Pinto Pereira Snehal M.,
Hipwell John H.,
McCormack Valerie A.,
Tanner Christine,
Moss Sue M.,
Wilkinson Louise S.,
Khoo Lisanne A. L.,
Pagliari Catriona,
Skippage Pippa L.,
Kliger Carole J.,
Hawkes David J.,
dos Santos Silva Isabel M.
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3457470
Subject(s) - mammography , affine transformation , image registration , landmark , artificial intelligence , nuclear medicine , breast cancer , computer science , computed tomography , medicine , computer vision , mathematics , radiology , image (mathematics) , cancer , geometry
Purpose: To compare and evaluate intensity‐based registration methods for computation of serial x‐ray mammogram correspondence. Methods: X‐ray mammograms were simulated from MRIs of 20 women using finite element methods for modeling breast compressions and employing a MRI/x‐ray appearance change model. The parameter configurations of three registration methods, affine, fluid, and free‐form deformation (FFD), were optimized for registering x‐ray mammograms on these simulated images. Five mammography film readers independently identified landmarks (tumor, nipple, and usually two other normal features) on pairs of diagnostic and corresponding prediagnostic digitized images from 52 breast cancer cases. Landmarks were independently reidentified by each reader. Target registration errors were calculated to compare the three registration methods using the reader landmarks as a gold standard. Data were analyzed using multilevel methods. Results: Between‐reader variability varied with landmark ( p < 0.01 )and screen( p = 0.03 ) , with between‐reader mean distance (mm) in point location on the diagnostic/prediagnostic images of 2.50 (95% CI 1.95, 3.15)/2.84 (2.24, 3.55) for nipples and 4.26 (3.43, 5.24)/4.76 (3.85, 5.84) for tumors. Registration accuracy was sensitive to the type of landmark and the amount of breast density. For dense breasts( ≥ 40 % ) , the affine and fluid methods outperformed FFD. For breasts with lower density, the affine registration surpassed both fluid and FFD. Mean accuracy (mm) of the affine registration varied between 3.16 (95% CI 2.56, 3.90) for nipple points in breasts with density 20%–39% and 5.73 (4.80, 6.84) for tumor points in breasts with density < 20 % . Conclusions: Affine registration accuracy was comparable to that between independent film readers. More advanced two‐dimensional nonrigid registration algorithms were incapable of increasing the accuracy of image alignment when compared to affine registration.