Robust neutrosophic fusion design for magnetic resonance (MR) brain images - Premalatha, R.; Dhanalakshmi, P. (Biomedical signal processing and control, 2023)
Fusion plays a pivotal role in the field of clinical processing; it could minimize the impacts of human errors, enhance diagnostic performance, and save manpower and time. Thus, the study examines the robust image fusion technique of MR brain images via a neutrosophic set (NS) subject to neutrosophic theory, wherein the effect of uncertainty in the frame of spatial and texture information is considered in the fusion design. Typically, the mechanism comprises four modules: (i) NS domain conversion; (ii) spatial feature extraction; (iii) texture feature extraction; and (iv) fusion. Primarily, we transform the input MR brain image into the field of NS, which consists of three subsets. Following the determined subsets, we apply spatial frequency to grab the spatial information present in the image. In particular, a grey level co-occurrence matrix (GLCM) is employed to describe the texture information of the addressed technique. After that, the fusion rule is applied to integrate both spatial and textural information from the input images, and then an addressed fusion design is derived. Subsequently, evaluation metrics are eventually offered to determine the significance as well as the efficacy of the suggested fusion design. •Propose a neutrosophic fusion design for magnetic resonance brain images.•Blending spatial and texture information to acquire a high-quality fused image.•Evaluation metrics are used to assess the performance of the addressed technique.•Extensive experimental analysis indicates the efficacy of our framework.