title: A multistage registration method using texture features.
abstract: We present a novel, multistage registration method based on Laws' texture features. In general, a large number of texture features may be extracted from the original intensity images. For each of the texture features, a criterion function that measures the similarity between the images may be derived. The proposed registration method consists of two major steps. In the first step, a dataset of images with the corresponding gold standard is used. In this step, the selection and ranking of the texture features for registration is made. The selection and ranking of the features is based on their robustness, accuracy, and capture range. The selected features are then entered in the second step, where the actual registration is performed using a sequence of registration stages. Our method is based on the selection of the most robust feature for the first registration stage and the selection of accurate feature(s) for the subsequent stages. The texture features are daisy-chained so that the accuracy of the previous feature is sufficient for the capture range of the next feature. We tested our method on 11 2D image pairs containing digital reconstructed radiographs and electron portal imaging modalities, which were difficult to register using intensity features alone. With our method, we have successfully registered 75% of the initial displacements, ranging from 5 to 7.5 mm, with the target-registration error below 3 mm, whereas the traditional intensity-based approach delivered only 15% successfully registered cases.
authors.affiliation: Sipronika d.o.o., Trzaska 2, SI-1000, Ljubljana, Slovenia. andreja.jarc@sipronika.si
authors.forename: Andreja
authors.initials: A
authors.lastname: Jarc
authors.forename: Janez
authors.initials: J
authors.lastname: Pers
authors.forename: Stanislav
authors.initials: S
authors.lastname: Kovacic
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D046709:Pattern Recognition, Physiological; D010388:Pelvis / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods