TY - JOUR
T1 - Locally adaptive Nakagami-based ultrasound similarity measures
AU - Wachinger, Christian
AU - Klein, Tassilo
AU - Navab, Nassir
N1 - Funding Information:
The work was partly funded by the European Commission. We are grateful to Mattias Hansson for helping with the experiments and active discussions.
PY - 2012/4
Y1 - 2012/4
N2 - 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.
AB - 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.
KW - Local adaptation
KW - Nakagami
KW - Registration
KW - Similarity measure
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=84856511687&partnerID=8YFLogxK
U2 - 10.1016/j.ultras.2011.11.009
DO - 10.1016/j.ultras.2011.11.009
M3 - Article
C2 - 22197152
AN - SCOPUS:84856511687
SN - 0041-624X
VL - 52
SP - 547
EP - 554
JO - Ultrasonics
JF - Ultrasonics
IS - 4
ER -