M3IF-NSST-MTV: Modified Total variation-based multi-modal medical image fusion using Laplacian energy and morphology in the NSST domain

Abstract

This paper presents a new multi-modal medical image fusion (M3IF) technique that fuses the medical images obtained from different medical imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single Photon Emission Computed Tomography (SPECT) or Positron Emission Tomography (PET), into a single image. This single image is enhanced and contains all the important information of the source images. This paper proposes a hybrid M3IF technique, i.e., M3IF-NSST-MTV, where input medical images are decomposed using Non-Subsampled Shearlet Transform (NSST). It decomposes the image into low frequency coefficients (LFCs), and high frequency coefficients (HFCs). The LFCs are fused using Laplacian energy, and HFCs are fused using morphology. The fused image obtained after applying inverse-NSST is directed to the modified Total Variation (TV), that refines the NSST output. This modified TV output is again fused with NSST output using Feature Similarity Index Measure (FSIM) with Correlation Coefficient (CC)-based threshold value. This modified TV refinement process is iterative process. The results of M3IF-NSST-MTV are evaluated at the pre-set number of iterations = 200. The final fusion results of M3IF-NSST-MTV are compared with some of the prevalent non-traditional methods and based on visual quality and quantitative metric-based analysis; it is found that the M3IF-NSST-MTV delivers better results than all the compared methods.