Patel Anjali, Gupta Deepa, Gopalakrishnan E A, Sasidharan Divya, Sowmya V, Zakariah Mohammed, Almazyad Abdulaziz S
Department of Computer Science and Engineering, Amrita School of Computing Bengaluru, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Department of Computer Science and Engineering, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Sci Rep. 2025 Jul 2;15(1):23548. doi: 10.1038/s41598-025-07831-8.
Intuitionistic fuzzy similarity measures (IFSMs) play a significant role in applications involving complex decision-making, pattern recognition, and image processing. Several researchers have introduced different methods of IFSMs, yet these IFSMs fail to provide rational decisions. Therefore, in this research, we present a novel IFSM by considering the global maximum and the minimum differences in membership, non-membership, and hesitancy degrees between two intuitionistic fuzzy sets (IFSs). We show that the proposed IFSM meets the fundamental properties and provide numerical examples to prove its superiority. We implement it to solve pattern recognition problems and demonstrate its applicability and feasibility by using the parameter 'degree of confidence' as a performance index. Additionally, an image fusion method using the proposed IFSM is developed in this work. To construct an image fusion algorithm, initially, we employ a two-layer decomposition method based on Gaussian filtering to the source images of different modalities to decompose them into the base subimages and the detailed subimages. Then, we use the proposed IFSM to extract the features of base subimages and define two fusion rules to fuse the base subimages and detailed subimages. Then, we show the applicability of this method by conducting extensive experiments using three different types of medical image datasets. We evaluated the effectiveness of the proposed image fusion method using six metrics: Mean, Standard Deviation, feature mutual information, Spatial Frequency, Average Gradient, and Xydeas. Experimental results reveal that the proposed IFSM and fusion approach achieve superior performance compared to most existing methods.
直觉模糊相似性度量（IFSM）在涉及复杂决策、模式识别和图像处理的应用中发挥着重要作用。几位研究人员已经介绍了不同的IFSM方法，但这些IFSM未能提供合理的决策。因此，在本研究中，我们通过考虑两个直觉模糊集（IFS）之间隶属度、非隶属度和犹豫度的全局最大和最小差异，提出了一种新颖的IFSM。我们表明所提出的IFSM满足基本属性，并提供数值示例以证明其优越性。我们将其应用于解决模式识别问题，并以“置信度”参数作为性能指标来证明其适用性和可行性。此外，本工作还开发了一种使用所提出的IFSM的图像融合方法。为了构建图像融合算法，首先，我们对不同模态的源图像采用基于高斯滤波的两层分解方法，将它们分解为基础子图像和细节子图像。然后，我们使用所提出的IFSM提取基础子图像的特征，并定义两个融合规则来融合基础子图像和细节子图像。然后，我们通过使用三种不同类型的医学图像数据集进行广泛实验来展示该方法的适用性。我们使用六个指标评估了所提出的图像融合方法的有效性：均值、标准差、特征互信息、空间频率、平均梯度和Xydeas。实验结果表明，与大多数现有方法相比，所提出的IFSM和融合方法具有卓越的性能。
