Multimodal medical image fusion based on content-based decomposition and PCA-Sigmoid.

OBJECTIVE The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/single-photon emission computed tomography) information. METHODS We proposed a new medical image fusion method based on content-based decomposition, principal component analysis (PCA), and sigmoid function. We considered empirical wavelet transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners. PCA is used to obtain initial weights corresponding to each detail layer. RESULTS In our experiments, we found that direct usage of PCA for detail layer fusion introduces severe artifacts into the fused image due to weight scaling issues. In order to tackle this, we considered using the sigmoid function for better weight scaling. We considered 24 pairs of MRI-PET and 24 pairs of MRI-SPECT images for fusion and the results are measured using four significant quantitative metrics. CONCLUSION Finally, we compared our proposed method with other state-of-the-art transform-based fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.