Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, India.
Health Care Manag Sci. 2020 Dec;23(4):661-669. doi: 10.1007/s10729-019-09492-2. Epub 2019 Jul 10.
In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. In this paper, we propose an efficient medical image fusion system based on discrete wavelet transform and binary crow search optimization (BCSO) algorithm. Here, we consider two different patterns of images as the input of the system and the output is the fused image. In this approach, at first, to enhance the image, we apply a median filter which is used to remove the noise present in the input image. Then, we apply a discrete wavelet transform on both the input modalities. Then, the approximation coefficients of modality 1 and detailed coefficients of modality 2 are combined. Similarly, approximation coefficients of modality 2 and detailed coefficients of modality 1 are combined. Finally, we fuse the two modality information using novel fusion rule. The fusion rule parameters are optimally selected using binary crow search optimization (BCSO) algorithm. To evaluate the performance of the proposed method, we used different quality metrics such as structural similarity index measure (SSIM), Fusion Factor (FF), and entropy. The presented model shows superior results with 6.63 of entropy, 0.849 of SSIM and 5.9 of FF.
在临床应用中，单模态图像不能提供足够的诊断信息。因此，有必要结合不同模态图像的优势或互补性。在本文中，我们提出了一种基于离散小波变换和二进制乌鸦搜索优化（BCSO）算法的高效医学图像融合系统。在这里，我们考虑两种不同模式的图像作为系统的输入，输出是融合后的图像。在这种方法中，首先，为了增强图像，我们应用中值滤波器来去除输入图像中的噪声。然后，我们对两种模态都进行离散小波变换。然后，将模态 1 的逼近系数和模态 2 的细节系数组合在一起。同样，将模态 2 的逼近系数和模态 1 的细节系数组合在一起。最后，使用新的融合规则融合两种模态的信息。融合规则参数使用二进制乌鸦搜索优化（BCSO）算法进行最优选择。为了评估所提出方法的性能，我们使用了不同的质量指标，如结构相似性指数度量（SSIM）、融合因子（FF）和熵。所提出的模型具有较高的性能，熵为 6.63，SSIM 为 0.849，FF 为 5.9。
