In the recent past, a medical 2D image fusion approach has played a vital role in the biomedical industry for diagnosing disease using various modalities of medical images. Biomedical 2D image fusion synthesis is a combining technique that merges useful information from two or more modalities of data captured through high definition scanning devices. The fused image is highly useful when doctors make emergency surgical decisions for their clinical patients. In this research, an innovative medical 2D image fusion approach called 2-Dimensional Double Density Wavelet Transform (2D-DDWT), based on maximization of scaling rule, is being proposed for the combination of CT and MRI images of human brain. The source image is applied to 2D-DDWT pursued by the fusion of sub images of coefficients based on high and low frequencies that are decomposed by transformation technique. The low frequency information is decomposed using the congruency rule of phase while the high frequency information is decomposed using 2D Gabor filter. The 2D-DDWT frequency coefficients are fused by using Principle Component Analysis (PCA) for the approximation parameters, and therefore the rule of selection maximum is being applied for the coefficients to improve features of image fusion such as segmentation. Maximization scaling rule is proposed for pixel level fusion to improve quality metrics. The quantitative and qualitative analysis proves the efficiency of the proposed methodology and demonstrates the improvement of the proposed methodology over existing methods.

中文翻译：

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使用二维双密度小波变换进行高效医学图像融合以提高质量指标


近年来，医学二维图像融合方法在生物医学行业使用各种医学图像诊断疾病方面发挥了至关重要的作用。生物医学二维图像融合合成是一种组合技术，可合并通过高清扫描设备捕获的两种或多种数据模态的有用信息。当医生为临床患者做出紧急手术决定时，融合图像非常有用。在这项研究中，提出了一种基于缩放规则最大化的创新医学二维图像融合方法，称为二维双密度小波变换（2D-DDWT），用于人脑 CT 和 MRI 图像的组合。将源图像应用于 2D-DDWT，通过变换技术分解的基于高频和低频的系数子图像的融合来实现。利用相位同余规则分解低频信息，利用2D Gabor滤波器分解高频信息。 2D-DDWT频率系数的融合采用主成分分析(PCA)作为近似参数，因此对系数应用选择最大值的规则，以改善分割等图像融合的特征。提出了像素级融合的最大化缩放规则，以提高质量指标。定量和定性分析证明了所提出方法的有效性，并证明了所提出方法相对于现有方法的改进。