A novel fusion method for X-ray phase contrast imaging based on fast adaptive bidimensional empirical mode decomposition

Abstract

BACKGROUNDS: X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. OBJECTIVE: To explore the application value of a novel image fusion method for a XPCI system and a computed tomography (CT) system. METHODS: The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions, which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. RESULTS: The δ-guided FABEMD-MGPCNN method achieved favorable results with biological samples, as compared to traditional image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT shows the expected results. CONCLUSIONS: The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over other traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.