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Multimodal image fusion using sparse representation classification in tetrolet domain

机译：四极管域中稀疏表示分类的多模式图像融合

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Multimodal medical image fusion, which aims at integrating different multimodal information into a single output, plays an important role in the clinical applicability of medical images such as noninvasive diagnosis and image-guided surgery. The main motivation of this study is to model the coefficient selection step of medical image fusion as a pattern recognition task. The proposed method first decomposes the source images by the tetrolet transform. Subsequently, different activity measures are used to extract salient features from patches of the tetrolet subbands. The features are then fed to the sparse un-mixing by variable splitting and augmented Lagrangian (SUnSAL) classifier. Coefficients to be incorporated into the fused image are chosen by the classifier. Finally, the cycle-spinning technique is exploited to avoid artifacts. Experimental results on three pairs of medical images validate the reliability and credibility of the proposed method in clinical applications when compared with four state-of-the-art fusion methods. More specifically, the proposed framework does not suffer from contrast reduction, color distortion and loss of fine details. (C) 2018 Elsevier Inc. All rights reserved.
机译：多式化医学图像融合，其旨在将不同的多模式信息集成到单个输出中，在诸如非侵入性诊断和图像引导的手术等医学图像的临床适用性中起重要作用。本研究的主要动机是将医学图像融合的系数选择步骤模拟为模式识别任务。所提出的方法首先通过四极管变换分解源图像。随后，不同的活动措施用于从四极管子带的贴片中提取突出特征。然后，通过可变分割和增强拉格朗日（Sunsal）分类器将该特征馈送到稀疏的未混合。要结合到融合图像中的系数由分类器选择。最后，利用循环纺纱技术以避免伪影。与四个最先进的融合方法相比，三对医学图像的实验结果验证了临床应用中所提出的方法的可靠性和可信度。更具体地，所提出的框架不会遭受对比减小，颜色变形和损失细节。 （c）2018年Elsevier Inc.保留所有权利。

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