Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor.

BACKGROUND

Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning.

OBJECTIVE

Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method.

METHOD

The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem.

RESULTS AND CONCLUSION

Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment.