Abstract
The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain for fusing whole-body PET and CT images. Firstly, the gradient average of each pixel is chosen as the linking strength of PCNN model to implement self-adaptability. Secondly, to improve the fusion performance, the novel sum-modified Laplacian (NSML) and energy of edge (EOE) are extracted as the external inputs of the PCNN models for low- and high-pass subbands, respectively. Lastly, the rule of max region energy is adopted as the fusion rule and different energy templates are employed in the low- and high-pass subbands. The experimental results on whole-body PET and CT data (239 slices contained by each modality) show that the proposed framework outperforms the other six methods in terms of the seven commonly used fusion performance metrics.
Highlights
Medical images are very significant to clinical diagnosis and treatment
The original Positron Emission Tomography (PET) and Computed Tomography (CT) images captured from devices are different from each other, such as the images sizes and the scale and geometric distortion, which would affect the effectiveness of fusion
A novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain is proposed for fusing the wholebody PET and CT images
Summary
Only one modality of image could not provide sufficient clinical information. It is necessary to use different modalities of images to provide complementary information to physicians for better diagnosis. Medical image fusion is the process of collaboratively combining the complementary information from multimodal source medical images into one single fused image for further process. The fused image is suitable for visual perception, analysis, and diagnosis which is of great clinical meaning [2, 3]. NSCT is a multiscale, multidirectional, and translation invariant transform [17, 18]. Different from contourlet transform [19], NSCT does not employ down-samplers/up-samplers so that it could ensure the translation invariance and could effectively represent the edge and contour information.
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