图像融合
保险丝(电气)
人工智能
计算机科学
压缩传感
稳健性(进化)
融合
计算机视觉
模式识别(心理学)
高斯分布
图像(数学)
语言学
哲学
生物化学
化学
物理
量子力学
电气工程
基因
工程类
作者
Zhaodong Liu,Hongpeng Yin,Yi Chai,Simon X. Yang
标识
DOI:10.1016/j.eswa.2014.05.043
摘要
Fusion of multimodal medical images increases robustness and enhances accuracy in biomedical research and clinical diagnosis. It attracts much attention over the past decade. In this paper, an efficient multimodal medical image fusion approach based on compressive sensing is presented to fuse computed tomography (CT) and magnetic resonance imaging (MRI) images. The significant sparse coefficients of CT and MRI images are acquired via multi-scale discrete wavelet transform. A proposed weighted fusion rule is utilized to fuse the high frequency coefficients of the source medical images; while the pulse coupled neural networks (PCNN) fusion rule is exploited to fuse the low frequency coefficients. Random Gaussian matrix is used to encode and measure. The fused image is reconstructed via Compressive Sampling Matched Pursuit algorithm (CoSaMP). To show the efficiency of the proposed approach, several comparative experiments are conducted. The results reveal that the proposed approach achieves better fused image quality than the existing state-of-the-art methods. Furthermore, the novel fusion approach has the superiority of high stability, good flexibility and low time consumption.
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