Registration Strategies and Similarity Measures for Three-dimensional Ultrasound Mosaicing1

Rationale and Objectives

The creation of two-dimensional (2D) ultrasound mosaics is becoming a common clinical practice with a high clinical value. The next step coming along with the increasing availability of 2D array transducers is the creation of three-dimensional mosaics. The correct alignment of multiple ultrasound images is, however, a complex task. In the literature of ultrasound registration, the alignment of two images has been often addressed, but not the alignment of multiple images. Therefore, we propose registration strategies for multiple image alignment and ultrasound specific similarity measures, which are able to cope with problems when aligning ultrasound images.

Materials and Methods

In this study, we investigate the following strategies for multiple image alignment: pairwise registration with a successive Lie group normalization and simultaneous registration, which urges the usage of multivariate similarity measures. We propose alternative multivariate extensions for similarity measures based on a maximum likelihood framework. Moreover, we take the inherent contamination of ultrasound images by speckle patterns into consideration by using alternative noise models based on multiplicative Rayleigh distributed noise. This leads us to ultrasound-specific similarity measures.

Results

We compare the performances of pairwise and simultaneous registration approaches for the mosaicing scenario. Bivariate similarity measures are highly overlap-dependent, so that they rather favor the total overlap of the images than their correct alignment. Using ultrasound-specific bivariate measures leads to better results; however, a local optimum at the total overlap remains. In contrast, the derived multivariate similarity measures have a clear and single optimum at the correct alignment of the volumes.

Conclusion

The experiments indicate that standard, pairwise registration techniques have problems by aligning multiple ultrasound images with partial overlap. We demonstrate that the usage of an ultrasound specific similarity measure leads to better results for pairwise registration. The highest robustness, however, can be achieved by using simultaneous registration with the developed multivariate similarity measures.

Key Words: 3D registration, 3D ultrasound simultaneous registration, mosaicing, multivariate similarity measures, ultrasound specific similarity measures