研究目的
To implement an intuitionistic fuzzy logic-based image fusion approach for medical images that suppresses noise, enhances input images, and merges them efficiently in Hue-Saturation-Intensity domain to handle uncertainties due to vagueness and ambiguity.
研究成果
The proposed intuitionistic fuzzy sets-based image fusion technique for medical images significantly improves the output fused image both visually and metrically by enhancing low-contrast input images and removing noise before fusion. It overcomes the limitations of the previous method regarding low-contrast and noisy images and resolves the problem of color change in fused images.
研究不足
The method may not be well suited for all types of medical images due to their noisy and low-contrasted appearance. The problem of color change in fused image lies when fusing colored and grayscale images.
1：Experimental Design and Method Selection:
The methodology involves pre-processing input images for noise suppression and contrast enhancement, converting them to HSI model, fuzzifying using fuzzy membership function, generating intuitionistic fuzzy images (IFIs) using entropy model, and applying fuzzy rules based on blackness and whiteness of images to fuse them.
2：Sample Selection and Data Sources:
The study uses 512 × 512 sized without reference, very low-contrast medical images including MRI, CT, PET, and Spectroscopy scans, some downloaded from 'The Whole Brain Atlas – Harvard Medical School'.
3：List of Experimental Equipment and Materials:
Non-local mean filter (NLMF) for noise removal and fuzzy logic-based histogram equalization for contrast enhancement.
4：Experimental Procedures and Operational Workflow:
The process includes pre-processing, conversion to HSI model, fuzzification, IFI generation, blockwise fusion based on blackness and whiteness, reconstruction, defuzzification, and conversion back to RGB domain.
5：Data Analysis Methods:
Quality metrics such as Spatial Frequency, Standard Deviation, and Objective Image Fusion Performance Measure (OIFP) are used to quantify the performance of the fusion strategy.
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