Hybrid feature-based Log-Demons registration for tumour tracking in 2-D liver ultrasound images

Traditional intensity-based registration methods are often insufficient for tumour tracking in time-series ultrasound, where the low signal-to-noise ratio significantly degrades the quality of the output images, and topological changes may occur as the anatomical structures slide in and out of the focus plane. To overcome these issues, we propose a hybrid feature-based Log-Demons registration method. The novelty of our approach lies in estimating a hybrid update deformation field from demons forces that carry voxel-based local information and regional spatial correspondences yielded by a block-matching scheme within the diffeomorphic Log-Demons framework. Instead of relying on intensities alone to drive the registration, we use multichannel Log-Demons, with channels representing features like intensity, local phase and phase congruency. Results on clinical data show that our method successfully registers various patient-specific cases, where the tumours are of variable visibility, and in the presence of shadows and topological changes.