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

Article type: Research Article

Authors: Wang, Yu | Wang, Yuanjun^{; *}

Affiliations: School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China

Correspondence: [*] Corresponding author: Yuanjun Wang, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China. E-mail: yjusst@126.com.

Abstract: 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.

Keywords: 3-D medical image, multi-modal image fusion, directional structure tensor, local entropy, detail component

DOI: 10.3233/XST-200684

Journal: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 1001-1016, 2020