Haribabu Maruturi, Guruviah Velmathi
School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India.
Diagnostics (Basel). 2023 Jul 10;13(14):2330. doi: 10.3390/diagnostics13142330.
Multimodal medical image fusion (MMIF) is the process of merging different modalities of medical images into a single output image (fused image) with a significant quantity of information to improve clinical applicability. It enables a better diagnosis and makes the diagnostic process easier. In medical image fusion (MIF), an intuitionistic fuzzy set (IFS) plays a role in enhancing the quality of the image, which is useful for medical diagnosis. In this article, a new approach to intuitionistic fuzzy set-based MMIF has been proposed. Initially, the input medical images are fuzzified and then create intuitionistic fuzzy images (IFIs). Intuitionistic fuzzy entropy plays a major role in calculating the optimal value for three degrees, namely, membership, non-membership, and hesitation. After that, the IFIs are decomposed into small blocks and then perform the fusion rule. Finally, the enhanced fused image can be obtained by the defuzzification process. The proposed method is tested on various medical image datasets in terms of subjective and objective analysis. The proposed algorithm provides a better-quality fused image and is superior to other existing methods such as PCA, DWTPCA, contourlet transform (CONT), DWT with fuzzy logic, Sugeno's intuitionistic fuzzy set, Chaira's intuitionistic fuzzy set, and PC-NSCT. The assessment of the fused image is evaluated with various performance metrics such as average pixel intensity (API), standard deviation (SD), average gradient (AG), spatial frequency (SF), modified spatial frequency (MSF), cross-correlation (CC), mutual information (MI), and fusion symmetry (FS).
多模态医学图像融合（MMIF）是将不同模态的医学图像合并为单个输出图像（融合图像）的过程，该融合图像包含大量信息以提高临床适用性。它有助于更好地进行诊断，并使诊断过程更加轻松。在医学图像融合（MIF）中，直觉模糊集（IFS）在提高图像质量方面发挥着作用，这对医学诊断很有用。本文提出了一种基于直觉模糊集的MMIF新方法。首先，对输入的医学图像进行模糊化处理，然后创建直觉模糊图像（IFI）。直觉模糊熵在计算隶属度、非隶属度和犹豫度这三个度的最优值方面起着主要作用。之后，将IFI分解为小块，然后执行融合规则。最后，通过去模糊化过程可以获得增强的融合图像。所提出的方法在主观和客观分析方面在各种医学图像数据集上进行了测试。该算法提供了质量更好的融合图像，并且优于其他现有方法，如主成分分析（PCA）、离散小波变换主成分分析（DWTPCA）、轮廓波变换（CONT）、带模糊逻辑的离散小波变换（DWT）、Sugeno直觉模糊集、Chaira直觉模糊集和非下采样轮廓波变换（PC-NSCT）。融合图像的评估使用各种性能指标，如平均像素强度（API）、标准差（SD）、平均梯度（AG）、空间频率（SF）、修正空间频率（MSF）、互相关（CC）、互信息（MI）和融合对称性（FS）。
