Chengfang Zhang
ABSTRACT
Medical image fusion uses specific techniques to complement the information from different modalities of medical images to facilitate more accurate assistance in clinical applications. In this paper, a convolutional dictionary learning (CDL-ACE) medical image fusion method based on adaptive contrast enhancement is proposed. The proposed method takes advantage of CDL-ACE to compensate for model mismatch, reduce artifact generation and better match visual observations. Eight pairs of brain images are tested, and 4 representative methods are also compared to verify the performance of our approach. The average values of MI, Q0, QMI,QTE and QNCIE are 3.4275, 0.3949, 0.6705, 0.4431, and 0.8090 for the “city” training set and 3.4282, 0.3915, 0.6703, 0.4430, and 0.8090 for the “fruit” training set, respectively. In addition, our method improves on each metric by 3.14%, 12.79%, 4.13%, 5.25%, and 0.04% and 3.16%, 11.83%,4.10%, 5.22%, and 0.04%, respectively, in comparison with a CNN. The experimental results show that the method can achieve impressive performance in both subjective and objective visual evaluations.
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