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Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor
1School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China.
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.
