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Evaluating the Impact of Image Enhancement on X-ray Diagnostics Using HE, CLAHE, and Fuzzy Enhancement Techniques
Subject area: Science,Engineering and Technology · Area of research: Image Processing
Abstract
Image enhancement is critical in medical imaging for improving the visibility of structures, which is essential for accurate diagnosis and treatment planning. This study compares three enhancement techniques?Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Fuzzy Enhancement?applied to the MURA X-ray image dataset. The performance of these techniques is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Shannon Entropy. The experimental results show that HE achieves a PSNR of 31.76 dB and an SSIM of 0.7130, indicating effective noise reduction and detail preservation. CLAHE, with a PSNR of 31.13 dB and an SSIM of 0.5645, significantly enhances local contrast, as reflected by the highest entropy value of 3.6847, but alters the image structure more than the other methods. Results indicate that Fuzzy Enhancement achieves the highest SSIM score of 0.9506, demonstrating superior perceptual similarity to the original images, while CLAHE shows the highest entropy value at 3.6847, suggesting enhanced detail and variability. HE leads in PSNR with a score of 31.76 dB, indicating effective noise reduction. These findings have practical implications for clinical practice by potentially improving the accuracy and reliability of medical diagnoses through enhanced image quality.
Keywords
Image Enhancement, Medical Imaging Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Fuzzy Enhancement
How to cite this paper
@article{1706072,
author = {Nay Thazin Htun, Khin Mo Mo Tun},
title = {Evaluating the Impact of Image Enhancement on X-ray Diagnostics Using HE, CLAHE, and Fuzzy Enhancement Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {1},
pages = {376-387},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706072.pdf},
abstract = {Image enhancement is critical in medical imaging for improving the visibility of structures, which is essential for accurate diagnosis and treatment planning. This study compares three enhancement techniques?Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Fuzzy Enhancement?applied to the MURA X-ray image dataset. The performance of these techniques is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Shannon Entropy. The experimental results show that HE achieves a PSNR of 31.76 dB and an SSIM of 0.7130, indicating effective noise reduction and detail preservation. CLAHE, with a PSNR of 31.13 dB and an SSIM of 0.5645, significantly enhances local contrast, as reflected by the highest entropy value of 3.6847, but alters the image structure more than the other methods. Results indicate that Fuzzy Enhancement achieves the highest SSIM score of 0.9506, demonstrating superior perceptual similarity to the original images, while CLAHE shows the highest entropy value at 3.6847, suggesting enhanced detail and variability. HE leads in PSNR with a score of 31.76 dB, indicating effective noise reduction. These findings have practical implications for clinical practice by potentially improving the accuracy and reliability of medical diagnoses through enhanced image quality.},
keywords = {Image Enhancement, Medical Imaging Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Fuzzy Enhancement},
month = {July},
}