An Effective PDE-based Thresholding for MRI Image Denoising and H-FCM-based Segmentation
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 44, Issue 8
Abstract
Image denoising and segmentation play a crucial role in computer graphics and computer vision. A good image-denoising method must effectively remove noise while preserving important boundaries. Various image-denoising techniques have been employed to remove noise, but complete elimination is often impossible. In this paper, we utilize Partial Differential Equation (PDE) and generalised cross-validation (GCV) within Adaptive Haar Wavelet Transform algorithms to effectively denoise an image, with the digital image serving as the input. After denoising, the image is segmented using the Histon-related fuzzy c-means algorithm (H-FCM), with the processed image serving as the output. The proposed method is tested on images exposed to varying levels of noise. The performance of image denoising and segmentation techniques is evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) of 77.42, Mean Squared Error (MSE) of 0.0011, and Structural Similarity Index (SSIM) of 0.7848. Additionally, segmentation performance is measured with a sensitivity of 99%, specificity of 98%, and an accuracy of 98%. The results demonstrate that the proposed methods outperform conventional approaches in these metrics. The implementation of the proposed methods is carried out on the MATLAB platform.
Authors and Affiliations
Sreedhar Kollem, Samineni Peddakrishna, P Joel Josephson, Sridevi Cheguri, Garaga Srilakshmi, Y Rama Lakshmanna
Keywords
Related Articles
- EP ID EP750724
- DOI 10.52756/ijerr.2024.v44spl.005
- Views 49
- Downloads 0
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