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A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images
Xiao-Xia Yin1, Sillas Hadjiloucas2, Le Sun3
1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
Journal of Healthcare Engineering
|March 18, 2022
Summary
This paper introduces rule-based fuzzy inference systems for image filtering. These fuzzy filtering algorithms enhance medical image analysis by preserving edges and crucial information for disease diagnosis.
Area of Science:
- Computer Science
- Image Processing
- Artificial Intelligence
Background:
- Fuzzy filtering is essential for processing complex image data.
- Understanding fuzzy filter design principles is critical for applications.
- Biomedical image analysis requires advanced filtering techniques.
Purpose of the Study:
- To present rule-based fuzzy inference systems for image filtering.
- To clarify different fuzzy filter designs and compare algorithms.
- To highlight the benefits of fuzzy filtering in biomedical image analysis.
Main Methods:
- Developing mathematical representations based on fuzzy concepts.
- Implementing rule-based fuzzy inference systems.
- Applying various fuzzy multichannel filtering approaches.
Main Results:
- Demonstrated effectiveness of fuzzy filtering in restoring corrupted images.
- Showcased the capability of fuzzy filters in edge preservation.
- Validated the utility of fuzzy filtering for accurate disease diagnosis.
Conclusions:
- Rule-based fuzzy inference systems offer robust solutions for image filtering.
- Fuzzy filtering significantly improves the quality and interpretability of medical images.
- These methods are vital for advancing diagnostic accuracy in healthcare.
