Abstract
In order to obtain the physiological information and key features of source images to the maximum extent, improve the visual effect and clarity of the fused image, and reduce the computation, a multi-modal medical image fusion framework based on feature reuse is proposed. The framework consists of intuitive fuzzy processing (IFP), capture image details network (CIDN), fusion, and decoding. First, the membership function of the image is redefined to remove redundant features and obtain the image with complete features. Then, inspired by DenseNet, we proposed a new encoder to capture all the medical information features in the source image. In the fusion layer, we calculate the weight of each feature graph in the required fusion coefficient according to the trajectory of the feature graph. Finally, the filtered medical information is spliced and decoded to reproduce the required fusion image. In the encoding and image reconstruction networks, the mixed loss function of cross entropy and structural similarity is adopted to greatly reduce the information loss in image fusion. To assess performance, we conducted three sets of experiments on medical images of different grayscales and colors. Experimental results show that the proposed algorithm has advantages not only in detail and structure recognition but also in visual features and time complexity compared with other algorithms.
Highlights
Multi-modal medical image fusion is a combination of images of the same tissue or organ from multiple sensors and doctors can obtain relevant physiological information of the tissue or organ and its metabolic status from the fused image
We propose a multi-modal medical image fusion model based on feature multiplexing
It has four main advantages: (1) Our model is the first model that is close to the application of multi-modal medical image fusion, that is, subjective evaluation is completely dependent on the prior knowledge of imaging, rather than relying on personal preference
Summary
Multi-modal medical image fusion is a combination of images of the same tissue or organ from multiple sensors and doctors can obtain relevant physiological information of the tissue or organ and its metabolic status from the fused image. The maturity of medical imaging technology provides various image information for medical diagnosis, including positron emission tomography (PET), computerized tomography (CT), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) [1]. Medical images of various models provide rich, intuitive, qualitative, and quantitative physiological information of the human body to doctors and researchers from the perspective of vision and become an important technical means to diagnose various diseases. CT images are sensitive to dense structures, such as bones or implants in the human body.
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