An Effective PDE-based Thresholding for MRI Image Denoising and H-FCM-based Segmentation

Sreedhar Kollem; Samineni Peddakrishna; P Joel Josephson; Sridevi Cheguri; Garaga Srilakshmi; Y Rama Lakshmanna

doi:10.52756/ijerr.2024.v44spl.005

Authors

Sreedhar Kollem Department of ECE, School of Engineering, SR University, Warangal, Telangana, India-506371; 2School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India-522237 https://orcid.org/0000-0002-9203-0404
Samineni Peddakrishna School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India-522237 https://orcid.org/0000-0002-5925-8124
P Joel Josephson Department of ECE, Malla Reddy Engineering College, Secunderabad, Telangana, India-500100 https://orcid.org/0000-0003-1804-8080
Sridevi Cheguri Department of ECE, St. Martins Engineering College, Secunderabad, Telangana, India-500100 https://orcid.org/0009-0004-2714-7245
Garaga Srilakshmi Department of ECE, Aditya College of Engineering and Technology, Surampalem, Andhra Pradesh, India-533437 https://orcid.org/0009-0005-5254-7766
Y Rama Lakshmanna Department of ECE, SRKR Engineering College, Bhimavaram, Andhra Pradesh- 534204 https://orcid.org/0000-0002-5356-477X

DOI:

https://doi.org/10.52756/ijerr.2024.v44spl.005

Keywords:

Adaptive Haar Wavelet Transform, Fuzzy C-means Clustering, Generalized cross-validation, Image denoising, Partial differential equation

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.

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