Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.

中文翻译：

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改进的基于全局花授粉算法的图像融合，用于使用计算机断层扫描和磁共振成像的医学诊断

近来，计算机断层摄影（CT）和磁共振成像（MRI）医学图像融合已经成为医学领域的一个具有挑战性的问题。最佳融合图像是轻松检测疾病的重要组成部分。在这项研究中，我们提出了一种用于CT和MRI图像融合的迭代优化方法。最初，CT和MRI图像融合面临多标签优化问题。主要目的是在图像融合期间最小化数据和平滑度成本。为了优化融合参数，提出了一种改进的全局花授粉算法。在此，根据不同的评估指标对六组具有不同实验分析的融合图像进行评估，例如准确性，特异性，敏感性，SD，结构相似性指标，特征相似性指标，互信息，融合质量，和均方根误差（RMSE）。与最先进的方法进行比较时，所提出的融合模型提供了具有更高融合性能的最佳RMSE。在一组医学数据的MRI和CT图像上进行的实验表明，所提出的方法在融合质量方面优于非常有竞争力的性能。