Ultrasound Specific Similarity Measures for Three-Dimensional Mosaicing

The introduction of 2D array ultrasound transducers enables the instantaneous acquisition of ultrasound volumes in the clinical practice. The next step coming along is the combination of several scans to create compounded volumes that provide an extended field-of-view, so called mosaics. The correct alignment of multiple images, which is a complex task, forms the basis of mosaicing. Especially the simultaneous intensity-based registration has many properties making it a good choice for ultrasound mosaicing in comparison to the pairwise one. Fundamental for each registration approach is a suitable similarity measure. So far, only standard measures like SSD, NNC, CR, and MI were used for mosaicing, which implicitly assume an additive Gaussian distributed noise. For ultrasound images, which are degraded by speckle patterns, alternative noise models based on multiplicative Rayleigh distributed noise were proposed in the field of motion estimation. Setting these models into the maximum likelihood estimation framework, which enables the mathematical modeling of the registration process, led us to ultrasound specific bivariate similarity measures. Subsequently, we used an extension of the maximum likelihood estimation framework, which we developed in a previous work, to also derive multivariate measures. They allow us to perform ultrasound specific simultaneous registration for mosaicing. These measures have a higher potential than afore mentioned standard measures since they are specifically designed to cope with problems arising from the inherent contamination of ultrasound images by speckle patterns. The results of the experiments that we conducted on a typical mosaicing scenario with only partly overlapping images confirm this assumption.