Feature-Level Image Fusion Scheme for X-Ray Multi-Contrast Imaging.

Since the mid-1990s, X-ray phase contrast imaging (XPCI) has attracted increasing interest in the industrial and bioimaging fields due to its high sensitivity to weakly absorbing materials and has gained widespread acceptance. XPCI can simultaneously provide three imaging modalities with complementary information, offering enriched details and data. This study proposes an image fusion method that simultaneously retrieves the three complementary channels of XPCI. It integrates block features, non-subsampled contourlet transform (NSCT), and a spiking cortical model (SCM), comprising three steps: (I) Image denoising, (II) Block-based feature-level NSCT-SCM fusion, and (III) Image quality enhancement. Compared with other methods in the XPCI image fusion field, the fusion results of the proposed algorithm demonstrated significant advantages, particularly with an impressive increase in the standard deviation by over 50% compared to traditional NSCT-SCM. The results revealed that the proposed algorithm exhibits high contrast, clear contours, and a short operation time. Experimental outcomes also demonstrated that the block-based feature extraction procedure performs better in retaining edge strength and texture information, with released computational resource consumption, thus, offering new possibilities for the industrial application of XPCI technology. [ABSTRACT FROM AUTHOR]