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Image segmentation is an extremely important process in the medical context. If the method used not only addresses segmentation but also allows for the generation of interpretations expressed in natural language, it can lead to significant contributions to the study and resolution of certain medical problems. This paper presents a method for interpreting magnetic resonance images (MRIs) based on the automatic generation of interval-valued membership functions (IVMFs) and their interpretation through specific measures applied to these functions. The analysis conducted in previous work is substantially expanded. A set of new measures on the IVMFs is proposed, allowing for the interpretation of MRI images in terms of pixel intensities across different sequences. An exhaustive analysis is carried out on brain MRIs in T1, T2, and PD sequences. Global transformations on the images are considered, including contrast stretching, brightness increase and decrease, histogram equalization, and noise addition. It is demonstrated that the generated IVMFs and the proposed measures reflect and explain the expected changes. It is concluded that the proposed method, based on fuzzy logic, is suitable for the interpretation of MRIs, and its extension to other types of images or its application to other types of data is proposed as future work.

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Global Transformations in Magnetic Resonance Imaging for Enhancing Knowledge Discovery Through Automated Membership Functions

  • 会议论文

  • 发表日期：

    2025年

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  • Diego S. Comas,
  • Guillermo N. Abras,
  • Gustavo J. Meschino,
  • Virginia L. Ballarin

摘要

Image segmentation is an extremely important process in the medical context. If the method used not only addresses segmentation but also allows for the generation of interpretations expressed in natural language, it can lead to significant contributions to the study and resolution of certain medical problems. This paper presents a method for interpreting magnetic resonance images (MRIs) based on the automatic generation of interval-valued membership functions (IVMFs) and their interpretation through specific measures applied to these functions. The analysis conducted in previous work is substantially expanded. A set of new measures on the IVMFs is proposed, allowing for the interpretation of MRI images in terms of pixel intensities across different sequences. An exhaustive analysis is carried out on brain MRIs in T1, T2, and PD sequences. Global transformations on the images are considered, including contrast stretching, brightness increase and decrease, histogram equalization, and noise addition. It is demonstrated that the generated IVMFs and the proposed measures reflect and explain the expected changes. It is concluded that the proposed method, based on fuzzy logic, is suitable for the interpretation of MRIs, and its extension to other types of images or its application to other types of data is proposed as future work.