Q-rung orthopair fuzzy framework for multimodal MRI image fusion and U-Net-based segmentation for brain tumor detection

Overview

1. Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India
2. MSU-BIT-SMBU Joint Research Center of Applied Mathematics, Shenzhen MSU-BIT University, Shenzhen, China
3. School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
4. Centre for Human Movement Analytics, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
5. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

Abstract

Introduction: Medical image analysis is a significant research domain in contemporary healthcare systems, which helps to accurately diagnose diseases and support clinical decision-making. This domain consists of important operations, including image enhancement, fusion, segmentation, morphological processing, and denoising, which assist in the effective understanding of complex biomedical information. Multimodal image fusion and segmentation are particularly important among these processes, as they enable the integration of complementary diagnostic information and the clear interpretation of clinically meaningful structures. However, medical images often consist of uncertainty, noise, and incomplete pixel information, creating major challenges for conventional analytical techniques.

Methods: To address these challenges, the present study introduces a q-rung orthopair fuzzy set (qROFS)-based framework for multimodal magnetic resonance imaging (MRI) fusion, subsequently a U-Net-based segmentation architecture for brain tumor detection. To effectively handle uncertainty and preserve important image information, the fusion strategy is designed based on q-rung orthopair fuzzy Hamacher t-norm and t-conorm operators to integrate complementary information in four different MRI modalities, including T1, T1ce, T2, and Flair. The resultant fused images are subsequently processed through a U-Net architecture to obtain precise tumor segmentation.

Results and discussion: Experiments conducted on the BraTS 2020 benchmark dataset demonstrate the effectiveness of the proposed framework. Across the four MRI modalities (T1, T1ce, T2, and Flair), the proposed fusion framework attains the best quantitative performance when compared to state-of-the-art methods with an SSIM of 0.9271, an MSSIM of 0.9481, a PSNR of 24.7130 dB, a low AMBE of 6.6779, a Correlation coefficient of 0.9583, an MI of 1.4097, and a UQI of 0.9767. A comparative evaluation with existing fuzzy set-based fusion models further assures the effectiveness of the proposed qROFS-based framework for multimodal MRI image fusion. Furthermore, the U-Net-based segmentation model achieves excellent performance, with an accuracy of 98.96%, a precision of 98.95%, a sensitivity of 98.94%, and a specificity of 99.65%.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/awsaf49, at Kaggle; found in the text, “Data description”

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 24 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 47 references.

Cite

This paper

Augustin, F., Nithyanandham, D., C V, M. K., M, D. R., Ramasamy, S., D, S., & V, P. (2026). Q-rung orthopair fuzzy framework for multimodal MRI image fusion and U-Net-based segmentation for brain tumor detection. Frontiers in artificial intelligence, 9, 1840304.

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