Three-band MRI Image Fusion Utilizing the Wavelet-based Method Optimized with Two Quantitative Fusion Metrics

摘要

In magnetic resonance imaging (MRI), there are three bands of images ("MRI triplet") available, which are T1-, T2- and PD-weighted images. The three images of a MRI triplet provide complementary structure information and therefore it is useful for diagnosis and subsequent analysis to combine three-band images into one. We propose an advanced discrete wavelet transform (aDWT) for three-band MRI image fusion and the aDWT algorithm is further optimized utilizing two quantitative fusion metrics - the image quality index (IQI) and ratio spatial frequency error (rSFe). In the aDWT method, principle component analysis (PCA) and morphological processing are incorporated into a regular DWT fusion algorithm. Furthermore, the aDWT has two adjustable parameters - the level of DWT decomposition (L_d) and the length of the selected wavelet (L_w) that determinately affect the fusion result. The fused image quality can be quantitatively measured with the established metrics - IQI and rSFe. Varying the control parameters (L_d and L_w), an iterative fusion procedure can be implemented and running until an optimized fusion is achieved. We fused and analyzed several MRI triplets from the Visible Human Project~® female dataset. From the quantitative and qualitative evaluations of fused images, we found that (1) the aDWTi-IQI algorithm produces a smoothed image whereas the aDWTi-rSFe algorithm yields a sharpened image, (2) fused image "T1+T2" is the most informative one in comparison with other two-in-one fusions (PD+T1 and PD+T2), (3) for three-in-one fusions, no significant difference is observed among the three fusions of (PD+T1)+T2, (PD+T2)+T1 and (T1+T2)+PD, thus the order of fusion does not play an important role. The fused images can significantly benefit medical diagnosis and also the further image processing such as multi-modality image fusion (with CT images), visualization (colorization), segmentation, classification and computer-aided diagnosis (CAD).