Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Abstract

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.

A Review of Multimodal Medical Image Fusion Techniques

“…It is the first example applied to medical image fusion. Ouerghi et al [27] proposed a simplified pulse-coupled neural network (S-PCNN) based on NSST. Unlike other fusion methods, this method converts PET images into YIQ components.…”

Fusion of Brain PET and MRI Images Using Tissue-Aware Conditional Generative Adversarial Network With Joint Loss

“…For the purpose of validating the performance of our proposed method, the following five state-of-the-art methods are used to compare with our method: the IHS combined with retina-inspired models (IHS-Retina) method [5], the non-subsampled shearlet transform (NSST) method [10], the low-rank sparse dictionaries learning (LSDL) method [11], the nonparametric density model (NDM) method [14], and the convolutional neural networks (CNNs) method [22].…”

“…This kind of approach usually generates a fused image which contains rich structural information with high resolution, but it generally distorts the color information of PET image due to the substitutive MRI image is rater different from the replaced I component of the PET image. The second type of method for merging PET and MRI images is implemented by the multi-resolution analysis (MRA) strategy [8]- [10]. This kind of method first decomposes PET and MRI images into multi-scale coefficients and transforming bases; then merges the decomposed coefficients according to a certain fusion rule; finally, inversely transforms the fused coefficients and transforming bases so as to get the final fused image.…”

Efficiency Evaluation of Cultural Services in the Czech Republic via Multi-Criteria Decision Analysis

“…The SD method allocates weights based on the variability of individual indicators, i.e., the relative moment characteristics, which is also often used in academia [64][65][66][67]. An indicator with the highest rate of absolute variability is evaluated as the most important, and its application is offered by the research of [68]. The MW method is the simplest in its process when the weight of all indicators is equal [69].…”