title: Locally adaptive Nakagami-based ultrasound similarity measures.
abstract: The derivation of statistically optimal similarity measures for intensity-based registration is possible by modeling the underlying image noise distribution. The parameters of these distributions are, however, commonly set heuristically across all images. In this article, we show that the estimation of the parameters on the present images largely improves the registration, which is a consequence of the more accurate characterization of the image noise. More precisely, instead of having constant parameters over the entire image domain, we estimate them on patches, leading to a local adaptation of the similarity measure. While this basic idea of creating locally adaptive metrics is interesting for various fields of application, we present the derivation for ultrasound imaging. The domain of ultrasound is particularly appealing for this approach, due to the inherent contamination with speckle noise. Furthermore, there exist detailed analyses of suitable noise distributions in the literature. We present experiments for applying a bivariate Nakagami distribution that facilitates modeling of several scattering scenarios prominent in medical ultrasound. Depending on the number of scatterers per resolution cell and the presence of coherent structures, different Nakagami parameters are required to obtain a valid approximation of the intensity statistics and to account for distributional locality. Our registration results on radio-frequency ultrasound data confirm the theoretical necessity for a spatial adaptation of similarity metrics.
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, München, Germany. wachinge@in.tum.de
authors.forename: Christian
authors.initials: C
authors.lastname: Wachinger
authors.forename: Tassilo
authors.initials: T
authors.lastname: Klein
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
mesh_terms: D000328:Adult; D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D016013:Likelihood Functions; D015233:Models, Statistical; D009333:Neck / Q000000981:diagnostic imaging*; D012542:Scattering, Radiation; D014159:Transducers; D014463:Ultrasonography / Q000379:methods*