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MRI and PET/SPECT image fusion at feature level using ant colony based segmentation

机译：使用基于蚁群的分割在特征级别进行MRI和PET / SPECT图像融合

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Extracting salient features from the medical images and combining them by an appropriate algorithm are the key challenges of multimodal image fusion. The commonly used coefficient-wise fusion may also inject noise into the merged images. To tackle the problem, this paper proposes a new method of multimodal image fusion which makes use of a segmentation map given by the ant colony algorithm. Firstly, the proposed method applies the maximum selection rule in ensemble empirical mode decomposition (EEMD) domain to obtain a fusion map. Then, the proposed approach exploits the color information of the pseudo-color image (PET or SPECT) to find spatial regions of pixels belonging to the same object. This step gives the segmentation map. Finally, the proposed method uses the majority voting process to combine the results of the fusion map and the segmentation map. In fact, the majority voting process determines the winner in each region and scale. The EEMD transform is used to decompose images because it is an adaptive and fully data-driven multiscale transform, and the ant colony algorithm is used for segmentation because it can yield a near optimal segmentation solution. Experimental fusion results are presented on three medical image datasets. It is shown in experiments that the proposed scheme improves the fusion results and provides images with more spatial and color information, when compared to state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.
机译：从医学图像中提取显着特征并通过适当的算法将其组合是多模式图像融合的关键挑战。常用的逐系数融合也可以将噪声注入到合并图像中。针对这一问题，本文提出了一种新的多模态图像融合方法，该方法利用了蚁群算法给出的分割图。首先，该方法将最大选择规则应用于整体经验模态分解（EEMD）域，以获得融合图。然后，提出的方法利用伪彩色图像（PET或SPECT）的颜色信息来查找属于同一对象的像素的空间区域。此步骤给出了分割图。最后，所提出的方法使用多数投票过程将融合图和分割图的结果进行组合。实际上，多数投票过程决定了每个地区和规模的赢家。 EEMD变换用于分解图像，因为它是一种自适应且完全由数据驱动的多尺度变换，而蚁群算法用于分段，因为它可以产生接近最佳的分段解决方案。实验融合结果显示在三个医学图像数据集上。实验表明，与最新方法相比，该方案可以改善融合效果，并为图像提供更多的空间和颜色信息。 （C）2018 Elsevier Ltd.保留所有权利。

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