Temporal subtraction in chest radiography: automated assessment of registration accuracy.
Radiologists routinely compare multiple chest radiographs acquired from the same patient over time to more completely understand changes in anatomy and pathology. While such comparisons are achieved conventionally through a side-by-side display of images, image registration techniques have been developed to combine information from two separate radiographic images through construction of a "temporal subtraction image." Although temporal subtraction images provide a powerful mechanism for the enhanced visualization of subtle change, errors in the clinical evaluation of these images may arise from misregistration artifacts that can mimic or obscure pathologic change. We have developed a computerized method for the automated assessment of registration accuracy as demonstrated in temporal subtraction images created from radiographic chest image pairs. The registration accuracy of 150 temporal subtraction images constructed from the computed radiography images of 72 patients was rated manually using a five-point scale ranging from "5-excellent" to "1-poor;" ratings of 3, 4, or 5 reflected clinically acceptable subtraction images, and ratings of 1 or 2 reflected clinically unacceptable images. Gray-level histogram-based features and texture measures are computed at multiple spatial scales within a "lung mask" region that encompasses both lungs in the temporal subtraction images. A subset of these features is merged through a linear discriminant classifier. With a leave-one-out-by-patient training/testing paradigm, the automated method attained an A(z) value of 0.92 in distinguishing between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. A second linear discriminant classifier yielded an A(z) value of 0.82 based on a feature subset selected from an independent database of digitized film images. These methods are expected to advance the clinical utility of temporal subtraction images for chest radiography.
Armato
Samuel G
SG
Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637, USA. s-armato@uchicago.edu
Doshi
Devang J
DJ
Engelmann
Roger
R
Croteau
Charles L
CL
MacMahon
Heber
H
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D013997:Time Factors