An emerging trend to deal with the issue of multimodality medical image fusion over the secure communication environment is required. Hence, this paper presents a human visual fusion algorithm for medical images such as computed tomography and magnetic resonance imaging which is based on the nonsubsampled shearlet transform (NSST) over the secure environment. Initially, the images are decayed through NSST into low and detailed highlights. The neighborhood aggregate of correlation‐based movement measures is proposed to intertwine the low frequency and sum‐modified‐Laplacian‐based fusion is utilized on detail subbands of NSST. After getting a fused image, a method noise thresholding approach is utilized to improve the accuracy of the proposed method. As a result, more accurate fused images is received. To measure the proposed method accuracy, the results of the fused images are also tested over the secure environment where encrypted/decrypted fused images are obtained using the random generator method. These decrypted fused images are also analyzed with original fused images with the proposed method as well as existing methods. From result analysis, it is observed that the proposed technique is better in holding a bone, calcification, cerebrospinal liquid, edema, and tumor subtleties of the source images and is more accurate than other existing fusion methods.

中文翻译：

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安全环境下多模态医学图像融合的新方法

需要一种在安全通信环境上处理多模态医学图像融合问题的新兴趋势。因此，本文提出了一种基于人眼视觉的医学图像融合算法，例如计算机断层扫描和磁共振成像，该算法基于安全环境下的非二次采样的小波变换（NSST）。最初，图像通过NSST衰减为低而详细的高光。提出了基于相关性的运动测度的邻域集合，以交织低频，并且在NSST的详细子带上使用了基于求和修正的Laplacian的融合。在获得融合图像之后，使用一种方法噪声阈值方法来提高所提出方法的准确性。结果，接收到更准确的融合图像。为了衡量建议的方法准确性，融合图像的结果也在安全环境中进行了测试，在该环境中，使用随机生成器方法获得了加密/解密的融合图像。还利用提出的方法以及现有方法将这些解密的融合图像与原始融合图像一起进行分析。从结果分析可以看出，所提出的技术在保留源图像的骨骼，钙化，脑脊液，水肿和肿瘤细微方面效果更好，并且比其他现有融合方法更准确。