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Medical-image-fusion algorithm based on a detail-enhanced and pulse-coupled neural-network model stimulated by parallel features

The purpose of medical-image fusion is to integrate the comprehensive information of multimodal medical images into a single image, which is helpful for clinical diagnosis, increasing the accuracy of disease observation by physicians, and shortening the treatment period. A new fusion algorithm for anatomical and functional images is proposed in this paper. Local Laplacian filtering (LLF) is chosen as the decomposition tool in the fusion process, which can enhance the details, protect the edges, and ensure that the details of the anatomical features in the fused images cannot be covered by the color information of the functional images. The steps of this algorithm are as follows: firstly, LLF decomposes the original image into an approximate image and a series of detailed images. Secondly, for the approximate image, this paper proposes a fusion rule for an improved local-energy maximum by combining the regional and edge energies. For detailed images, the parameter-adaptive simplified-pulse-coupled neural-network (PA-SPCNN) model is used to fuse the detailed images. The novel sum-modified-Laplacian and color-saliency feature are selected as external-stimulus inputs of the PA-SPCNN model in the anatomical and functional images, respectively. Finally, the fusion image is obtained by an inverse-LLF transform. Simulation experiments show that the proposed algorithm is superior to the classical algorithm in subjective and objective evaluation.