Multimodality medical image fusion algorithm based on gradient minimization smoothing filter and pulse coupled neural network

•GMSF and PCNN is introduced for multimodality medical image fusion.•A GMSF based framework is proposed to multi-scale decompose each source image.•Regional weighted sum energy is used to fuse base images.•More features of detail images are transmitted to the fused image by using PCNN.•The proposed...

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Vydáno v:Biomedical signal processing and control Ročník 30; s. 140 - 148
Hlavní autoři: Liu, Xingbin, Mei, Wenbo, Du, Huiqian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2016
Témata:
Computed tomography (CT)
Magnetic resonance imaging (MRI)
Medical image fusion
Multi-scale edge-preserving filter
Pulse coupled neural network
Medical image fusion
Magnetic resonance imaging (MRI)
Multi-scale edge-preserving filter
Computed tomography (CT)
Pulse coupled neural network
ISSN:1746-8094
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Popis
Shrnutí:•GMSF and PCNN is introduced for multimodality medical image fusion.•A GMSF based framework is proposed to multi-scale decompose each source image.•Regional weighted sum energy is used to fuse base images.•More features of detail images are transmitted to the fused image by using PCNN.•The proposed scheme outperforms classical and state-of-the-art methods. We propose a novel multimodality medical image fusion algorithm which involves L0 gradient minimization smoothing filter (GMSF) and pulse coupled neural network (PCNN). Firstly, an excellent multi-scale edge-preserving decomposition framework based on GMSF is proposed to decompose each source image into one base image and a series of detail images. For extracting and preserving more salient features and detail information, different fusion rules are designed to fuse the separated subimages. The base images are fused using the regional weighted sum of pixel energy and gradient energy, and a biologically inspired feedback neural network is used to fuse the detail images. The final fused image is obtained by synthesizing the fused base image and detail images. Experimental results on several datasets of CT and MRI images show that the proposed algorithm outperforms other compared methods in terms of both subjective and objective assessment.
ISSN:1746-8094
DOI:10.1016/j.bspc.2016.06.013