Regularized directional representations for medical image registration
This work addresses the challenge of accurate image registration in medical imaging, which is crucial for applications like diagnosis and treatment planning, by proposing an incremental improvement that can be easily integrated into existing frameworks.
The paper tackles the problem of improving medical image registration accuracy by aligning regularized vector fields derived from structural information, rather than directly aligning images, and demonstrates favorable performance compared to conventional intensity-based methods across multiple public datasets and imaging modalities.
In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an increasing number of works have demonstrated that substantial gains in registration accuracy can also be achieved by aligning structural representations of images rather than images themselves. Following this research path, we propose a new method for mono- and multimodal image registration based on the alignment of regularized vector fields derived from structural information such as gradient vector flow fields, a technique we call \textit{vector field similarity}. Our approach can be combined in a straightforward fashion with any existing registration framework by substituting vector field similarity to intensity-based registration. In our experiments, we show that the proposed approach compares favourably with conventional image alignment on several public image datasets using a diversity of imaging modalities and anatomical locations.