Self-learning based medical image representation for rigid real-time and multimodal slice-to-volume registration

Fast X-ray/CT image registration based on perspective projection triangular features

X-ray/CT image registration plays a pivotal role in enhancing surgical navigation success rates. However, challenges stemming from sparse and noisy X-ray image features, coupled with the complexities of multi-parameter optimization, impose limitations on existing methods in terms of registration accuracy and efficiency. In response, this paper presents an innovative approach-a fast X-ray/CT image registration method based on perspective projection triangular features(F-PPTF). By leveraging the conformal nature of perspective projection, the proposed method constructs perspective projection triangular features with rotation, translation, and scale invariance using point feature descriptors. Diverging from multi-parameter iterative optimization techniques, this approach achieves the decoupling of the six transformation parameters. This decoupling simplifies computational intricacies, thereby facilitating swift registration. Experimental evaluations conducted on synthetic and real X-ray images reveal an average rotational absolute error of 0.41°, an average translational absolute error of 1.16 mm, and an average registration time of 14.89 s. In comparison to conventional registration methodologies, the method presented in this paper demonstrates pronounced superiority in terms of both registration accuracy and efficiency, thereby exhibiting heightened potential for broader applicability.

Symmetry-based representation for registration of multimodal images

We propose a new two-dimensional structural representation method for registration of multimodal images by using the local structural symmetry of images, which is similar at different modalities. The symmetry is measured in various orientations and the best is mapped and used for the representation image. The optimum performance is obtained when using only two different orientations, which is called binary dominant symmetry representation (BDSR). This representation is highly robust to noise and intensity non-uniformity. We also propose a new objective function based on L2 distance with low sensitivity to the overlapping region. Then, five different meta-heuristic algorithms are comparatively applied. Two of them have been used for the first time on image registration. BDSR remarkably outperforms the previous successful representations, such as entropy images, self-similarity context, and modality-independent local binary pattern, as well as mutual information-based registration, in terms of success rate, runtime, convergence error, and representation construction.