Abstract
Abstract. In medical image processing, image fusion is the process of combining complementary information from different (multimodality) images to obtain a fused image, which plays a vital role in further analysis and treatment planning. The main idea of this paper is to improve the image content by fusing computer tomography (CT) and magnetic resonance (MR) images. We propose here the new algorithm based on the probabilistic gamma-normal model with structure-transferring properties. Firstly, we select the areas with the highest pixel intensity on original CT and MR images. In parallel with this, the structures of original images are distinguished using the probabilistic gamma-normal model. The weighted-fusion image can be obtained based on detected objects and structure. Finally, we smooth the weighted-fusion image using the structure-transferring filter and combine the smoothed image with the weighted-fusion image for obtaining the resulting image. The key point here is that we do not need to re-allocate the structure, which leads to the reduction of computation time. The proposed method gives the best result in terms of the spatial frequency metric and lower computation time than other image fusion methods.
Highlights
The fast development of computer technology promotes the progress of medical devices and medical imaging
Image fusion is the process of combining complementary information from different images to obtain a fused image, which plays a vital role to improve in medical image analysis and treatment planning
Pair computed tomography (CT) and magnetic resonance (MR) images are made for the same person and the same conditions
Summary
The fast development of computer technology promotes the progress of medical devices and medical imaging. The paper (Wang, Yang, 2020) can be mentioned among the latest works, where features of intensity and geometric structure of images are extracted using the saliency detection method and the structure tensor, and they are combined into a fusion image, which is processed by a filter based on the variational model. Such methods show high processing quality, but they have high computational complexity. Contrast features (areas) are used to detect object’s intensity information, and structures are an effective tool to describe image geometry
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