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MRI Image Fusion Based on Optimized Dictionary Learning and Binary Map Refining in Gradient Domain

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Abstract

The insufficient ability of edge feature extraction and high complexity limit the ability of sparse representation to obtain better medical image fusion performance. In this letter, we propose a novel multimodal medical image fusion method with optimized dictionary learning and binary map refining. The optimized dictionary learning uses loop iterations between separable FISTA and manifold-based conjugate gradient algorithm to catch detail texture features in detail layer, and the binary map refining solution adopts Gabor energy measurement with GDGIF to reserve structure and brightness characteristics in base layer. Experimental results of various medical images and clinical applications indicate the effectiveness of the proposed method.

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Abbreviations

GDGIF:

Gradient Domain Guided Image Filtering

CT:

Computerized Tomography

MR:

Magnetic Resonance

PET:

Positron Emission Tomography

SPECT:

Single-Photon Emission Computed Tomography

HVS:

Human Visual System

MST:

Multi-Scale Transform

LP:

Laplacian Pyramid

MGA:

Multiscale Geometric Analysis

NSCT:

Nonsubsampled Contourlet Transform

NSST:

Nonsubsampled Shearlet Transform

RGF:

Rolling Guidance Filter

GIF:

Guided Image Filter

SR:

Sparse Representation

K-SVD:

K-means Generalized Singular Value Decomposition

FISTA:

Fast Iterative Shrinkage-Thresholding Algorithm

OMP:

Orthogonal Matching Pursuit

WBAMI:

Whole Brain Atlas Midical Image

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Funding

This work was supported by the Natural Science Foundation of China [Grant No. 62172030]; the Fundamental Research Funds for the Central Universities [Grant No. 2021JBM009]; Youth Science Foundation Project of China [Grant No. 61902371]; the Natural Science Foundation of China [Grant No. 62002208].

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Qiu Hu, Shaohai Hu & Xiaole Ma

  2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, 100044, China

    Qiu Hu, Shaohai Hu & Xiaole Ma

  3. Key Laboratory of Solar Activity, National Astronomical Observatory, Chinese Academy of Science, Beijing, 100101, China

    Fengzhen Zhang

  4. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China

    Jing Fang

Authors
  1. Qiu Hu
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  2. Shaohai Hu
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  3. Xiaole Ma
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  4. Fengzhen Zhang
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  5. Jing Fang
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Correspondence to Shaohai Hu.

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Hu, Q., Hu, S., Ma, X. et al. MRI Image Fusion Based on Optimized Dictionary Learning and Binary Map Refining in Gradient Domain. Multimed Tools Appl 82, 2539–2561 (2023). https://doi.org/10.1007/s11042-022-12225-3

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  • DOI: https://doi.org/10.1007/s11042-022-12225-3

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