Tri-Modal Medical Image Fusion and Denoising Based on BitonicX Filtering

Medical image fusion integrates beneficial information from images of different modes and has been studied as a crucial auxiliary medical technology; however, current research mainly focused on two-modal fusion problems [e.g., computed tomography (CT)-magnetic resonance imaging (MRI), MRI-positron emission tomography (PET)], and little attention was paid to tri-modal medical image fusion (e.g., CT-MR_T2-single photon emission CT (SPECT), MR_T2-MR_Gad-SPECT). In this article, we present a tri-modal medical image fusion and denoising method that contains fusion models (I) and (II). A three-layer decomposition based on a bitonicX filter was designed for model (I). Particularly, the adaptive morphological gradient pulse-coupled neural network (AM-PCNN) and the coupled neural P (CNP) system-based rules were proposed to fuse the detail and texture layers, respectively. The middle-fused result was obtained by synthesizing the prefused energy, texture, and detail layers in the model (I). In model (II), for the fusion of the functional image and middle-fused result, we performed a “detail-energy” decomposition with the bitonicX filter. We also propose a gradient energy clarity (GEC) operator and energy-based rules for fusing the detail and energy layers. The final fused result was obtained by adding the prefused detail layer and the energy layer in the model (II). Numerous experiments were conducted to assess the fused images objectively and subjectively, demonstrating that the proposed method developed in this study surpasses the state-of-the-art methods in noise-free and noise fusions. Meanwhile, the average levels of the proposed method are 38.39%, 2.46%, 1.01%, 36.28%, 11.23%, 20.82%, 8.46%, and 2.52% higher than that of the compared methods evaluated by the metrics, including interactive information metric (QMI), Tsallis entropy metric (QTE), noncorrelated information entropy metric (QNCIE), phase consistency metric (QP), Chen-Blum metric (QCB), structural similarity metric (QSSIM), contrast-to-noise (CNR), and correlation coefficient (CC), respectively. Our code is available at
https://github.com/JEI981214/Tri-modal-bitonicX-based
.