Multi-modal medical image fusion via multi-dictionary and truncated Huber filtering

Jie, Yuchan; Li, Xiaosong; Tan, Haishu; Zhou, Fuqiang; Wang, Gao

doi:10.1016/j.bspc.2023.105671

Cited by 5 publications

(1 citation statement)

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“…The suggested algorithm has been experimented with various input image pairs in order to analyse its performance and accuracy. We compared the performance of our proposed framework with the following methods: multi-level morphological gradient (MLMG)-PCNN [ 16 ], joint bilateral filter (JBF) [ 18 ], Local extreme map Guided filter [ 39 ], LRD [ 7 ], adaptive co-occurence filter (ACOF) [ 40 ], multiple dictionaries and truncated Huber filtering (MDHU) [ 41 ], fast guided filtering (FGF) [ 42 ] and a fusion method based on phase congruency and local Laplacian energy [ 43 ]. In our experiments, subjective and objective evaluation is performed to assess the method’s efficiency.…”

Section: Discussionmentioning

confidence: 99%

Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering

Moghtaderi,

Einlou,

Wahid

et al. 2024

R. Soc. Open Sci.

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With the rapid development of medical imaging methods, multimodal medical image fusion techniques have caught the interest of researchers. The aim is to preserve information from diverse sensors using various models to generate a single informative image. The main challenge is to derive a trade-off between the spatial and spectral qualities of the resulting fused image and the computing efficiency. This article proposes a fast and reliable method for medical image fusion depending on multilevel Guided edge-preserving filtering (MLGEPF) decomposition rule. First, each multimodal medical image was divided into three sublayer categories using an MLGEPF decomposition scheme: small-scale component, large-scale component and background component. Secondly, two fusion strategies—pulse-coupled neural network based on the structure tensor and maximum based—are applied to combine the three types of layers, based on the layers' various properties. The three different types of fused sublayers are combined to create the fused image at the end. A total of 40 pairs of brain images from four separate categories of medical conditions were tested in experiments. The pair of images includes various case studies including magnetic resonance imaging (MRI) , TITc, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). We included qualitative analysis to demonstrate that the visual contrast between the structure and the surrounding tissue is increased in our proposed method. To further enhance the visual comparison, we asked a group of observers to compare our method’s outputs with other methods and score them. Overall, our proposed fusion scheme increased the visual contrast and received positive subjective review. Moreover, objective assessment indicators for each category of medical conditions are also included. Our method achieves a high evaluation outcome on feature mutual information (FMI), the sum of correlation of differences (SCD), Qabf and Qy indexes. This implies that our fusion algorithm has better performance in information preservation and efficient structural and visual transferring.

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