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Performance Analysis of Laplacian of Gaussian and Chebyshev Filters in Medical Image Edge Detection
Abstract
In the medical field, image analysis is essential for diagnosis and patient care planning. This study compares two edge detection methods: the Laplacian of Gaussian (LoG) filter and the Chebyshev filter. The LoG filter combines Gaussian smoothing with the Laplacian operator for noise reduction and smooth edge detection, while the Chebyshev filter, known for sharp edges, allows for adjustable frequency response but is more sensitive to noise. Medical images were pre-processed to reduce artifacts and noise before applying each filter. The performance metrics, including PSNR, SSIM, and EPI, were used to evaluate the results. The Chebyshev filter achieved the highest PSNR of 20.2202 and SSIM of 0.48089 for Meningioma with a low-pass filter of order 2. The LoG filter's highest PSNR was 12.5731 and SSIM was 0.99876 for Meningioma at sigma 0.5. The highest EPI for the Chebyshev filter was 0.13067 for Pituitary with a low-pass filter of order 2, compared to the LoG filter's highest EPI of 0.39688 for Glioma at sigma 1.5. These results guide the selection of edge detection methods, enhance diagnostic accuracy and patient care planning, and contribute to advanced medical image processing technologies.
Keywords
Laplacian of Gaussian (LoG);Filter Chebyshev Filter; PSNR; SSIM; EPI
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DOI: https://doi.org/10.26905/jeemecs.v7i2.13748
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