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A Novel Framework using Intuitionistic Fuzzy Logic with U-Net and U-Net++ Architecture: A case Study of MRI Bain Image Segmentation

Hanuman Verma, Kiho Im, Akshansh Gupta, M. Tanveer

Abstract

Accurate segmentation of brain images from magnetic resonance imaging (MRI) scans plays a pivotal role in brain image analysis and the diagnosis of neurological disorders. Deep learning algorithms, particularly U-Net and U-Net++, are widely used for image segmentation. However, it finds difficult to deal with uncertainty in images. To address this challenge, this work integrates intuitionistic fuzzy logic into U-Net and U-Net++, propose a novel framework, named as IFS U-Net and IFS U-Net++. These models accept input data in an intuitionistic fuzzy representation to manage uncertainty arising from vague ness and imprecise data. This approach effectively handles tissue ambiguity caused by the partial volume effect and boundary uncertainties. To evaluate the effectiveness of IFS U-Net and IFS U-Net++, experiments are conducted on two publicly available MRI brain datasets: the Internet Brain Segmentation Repository (IBSR) and the Open Access Series of Imaging Studies (OASIS). Segmentation performance is quantitatively assessed using Accuracy, Dice Coefficient, and Intersection over Union (IoU). The results demonstrate that the proposed architectures consistently improve segmentation performance by effectively addressing uncertainty

A Novel Framework using Intuitionistic Fuzzy Logic with U-Net and U-Net++ Architecture: A case Study of MRI Bain Image Segmentation

Abstract

Accurate segmentation of brain images from magnetic resonance imaging (MRI) scans plays a pivotal role in brain image analysis and the diagnosis of neurological disorders. Deep learning algorithms, particularly U-Net and U-Net++, are widely used for image segmentation. However, it finds difficult to deal with uncertainty in images. To address this challenge, this work integrates intuitionistic fuzzy logic into U-Net and U-Net++, propose a novel framework, named as IFS U-Net and IFS U-Net++. These models accept input data in an intuitionistic fuzzy representation to manage uncertainty arising from vague ness and imprecise data. This approach effectively handles tissue ambiguity caused by the partial volume effect and boundary uncertainties. To evaluate the effectiveness of IFS U-Net and IFS U-Net++, experiments are conducted on two publicly available MRI brain datasets: the Internet Brain Segmentation Repository (IBSR) and the Open Access Series of Imaging Studies (OASIS). Segmentation performance is quantitatively assessed using Accuracy, Dice Coefficient, and Intersection over Union (IoU). The results demonstrate that the proposed architectures consistently improve segmentation performance by effectively addressing uncertainty
Paper Structure (13 sections, 4 equations, 8 figures, 4 tables)

This paper contains 13 sections, 4 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Intuitionistic fuzzy sets and intuitionistic fuzzy representation of data
  3. Intuitionistic fuzzification of image
  4. Proposed approach
  5. Intuitionistic representation of data in triplet form
  6. Incorporation of intuitionistic fuzzy logic
  7. Intuitionistic fuzzy set with U-Net ( IFS_U-Net)
  8. Intuitionistic fuzzy logic with U-Net++ ( IFS_U-Net++)
  9. Experiment
  10. Dataset
  11. Baseline and implementation
  12. Results and discussion
  13. Conclusion

Figures (8)

  • Figure 1: Intuitionistic fuzzy data in form of (a): membership, (b): non-membership and (c): hesitation degree. The image corresponding to the membership degree shows a higher degree of belonging, while the image corresponding to non-membership degree highlight the respective the degree of non-belongingness, observable through the image contrast value. The image corresponding to the hesitation degree highlights the boundary region, capturing the uncertainty.
  • Figure 2: Gray-level histogram corresponding to (a): membership degree image (b): non-membership image and (c) hesitation image.
  • Figure 3: An overview of the proposed method in the deep learning architecture. The schematic overview of the training process of intuitionistic fuzzy set (IFS) system representation and deep learning architecture for segmentation of brain image. In this process, IFS system converted the input image into the triplet (intuitionistic fuzzy set) form that is membership $(\mu)$, non-membership $(\nu)$ and hesitation degree $(\pi)$. The uncertainty features in the image exploited the qualitative information using intuitionistic fuzzy logic in comparison to classical representation of an image. It utilizes the uncertainty during the training process.
  • Figure 4: IFS_U-Net framework with intuitionistic fuzzy logic systems. It accepts the input image in term of intuitionistic fuzzification of data and utilizes the uncertainty during the training process. And as a result, it able to segment the brain tissues in more accurate.
  • Figure 5: Framework for IFS_U-Net++ with intuitionistic fuzzy logic systems, where the input image accepted in term of intuitionistic fuzzification of data and utilizes the uncertainty. The input image $(X \times Y \times 1)$ converted in form of $(X \times Y \times 3)$) as the input for IFS_U-Net++ architecture
  • ...and 3 more figures