Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non‐subsampled shearlet transform (NSST) and simplified pulse‐coupled neural network model (S‐PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y‐component of PET image are decomposed into low‐frequency (LF) and high‐frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S‐PCCN which is motivated by an adaptive‐linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.
Abstract:In this paper, we propose a novel multimodal MRI and CT images fusion method based on Multi-resolution Singular Value Decomposition (MSVD) and Modified Pulse Coupled Neural Network (MPCNN).Firstly, the input pre-registered MRI and CT images are decomposed into high frequency (HF) and low frequency (LF) sub-bands by using the MSVD transform. Then, the MPCNN model is applied on each LF sub-bands. The proposed method can adaptively determine the linking strength of the MPCNN model. After that, LF coefficients are combined based on the output of MPCNN coefficients while HF coefficients are fused by using the maximum selection rule. Finally, the inverse MSVD is applied to reconstruct the fused image. Visual effect and objective evaluation criteria are used to evaluate the performance of our approach for nine pairs of MRI and CT images. The experimental results demonstrate that the proposed method has a better performance than other current methods.
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