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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 10 | Oct 2025

p-ISSN: 2395-0072

www.irjet.net

Biomedical Image Denoising and Compression using Image Processing Miss. Dipali Chougule1, Dr. Kishor Pandyaji2 1Student, 2Professor Département Of Electronics Engineering Dr. V.P.S.S.Ms Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon, Sangli, Maharashtra 416304 India. ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - The proliferation of high-resolution medical

The Discrete Wavelet Transform (DWT) offers a superior alternative to traditional Fourier-based methods. Unlike the Fourier transform, which provides only a frequencyamplitude representation and loses time information, the wavelet transform delivers a time-frequency representation of the signal. This allows for multi-resolution decomposition, analyzing the image at different scales. Wavelets are mathematical functions that decompose data into different frequency components, enabling high energy compaction and sparse signal representation. This property is instrumental for both compression and denoising.

imaging techniques like Computed Tomography (CT) and Magnetic Resonance (MR) generates vast amounts of data, posing significant challenges for storage and transmission. This paper presents a robust methodology for biomedical image denoising and compression using the Discrete Wavelet Transform (DWT). The proposed approach leverages the multi-resolution analysis capability of wavelets to effectively separate noise from critical image features and to achieve high compression ratios without perceptible loss of diagnostic quality. The core of the system involves a detailed image processing pipeline implemented in MATLAB's Wavelet Toolbox. Various wavelet filters, including Biorthogonal and Daubechies families, are applied to biomedical images. The system's performance is quantitatively evaluated using the Peak Signal-to-Noise Ratio (PSNR). Experimental results demonstrate that the DWT-based method successfully denoises images, preserving edges and critical details while achieving significant compression. This software-centric solution provides a practical and efficient approach for handling voluminous medical image data, facilitating quicker transmission and more economical storage in healthcare informatics.

This paper details the development and implementation of a DWT-based system for biomedical image denoising and compression. The primary objective is to minimize image file size and reduce noise to an acceptable level without compromising diagnostic quality. The methodology involves applying different wavelet functions to biomedical images, calculating PSNR values for each, and determining the optimal wavelet filter for this specific application. The entire process is executed within the MATLAB environment, utilizing its powerful Wavelet Toolbox to create a practical, software-based solution for healthcare challenges.

2. Literature Survey

Key Words: Biomedical Image Processing, Discrete Wavelet Transform (DWT), Image Denoising, Image Compression, Peak Signal-to-Noise Ratio (PSNR), MATLAB.

The field of biomedical image processing is analogous to biomedical signal processing but in multiple dimensions. A significant challenge in medical imaging is the presence of high levels of noise. Research has consistently shown that the use of the wavelet transform significantly improves image quality by reducing noise [1]. The core objective of any denoising algorithm is to eliminate unwanted noise while preserving the image's important features, a task for which wavelets are exceptionally well-suited.

Introduction

A computer revolution has profoundly impacted the medical field, where vast amounts of information must be processed quickly and accurately. High-resolution scanning techniques such as digital radiography, CT, and MR produce images containing crucial information for medical analysis. However, the enormous data volume generated by these modalities presents a significant problem from both storage and network transmission perspectives. To mitigate these issues, efficient data compression techniques are essential.

Early and influential work by Chang et al. [2] explored the connection between lossy compression and denoising, demonstrating that coefficient quantization in compression approximates wavelet thresholding for noise removal. They further developed BayesShrink, an adaptive wavelet thresholding technique within a Bayesian framework, which outperformed other contemporary methods [3]. The selection of the appropriate wavelet filter is critical for performance. Dilmaghani et al. [4] investigated the effect of different wavelet filters from orthogonal and biorthogonal families on medical image quality. Their findings indicated that regularity and linearity of the phase response of filters

Medical images are often corrupted by noise during acquisition and transmission, which can obscure critical diagnostic features. Image denoising is therefore a fundamental task in medical image processing, aiming to suppress noise while preserving essential structural information like edges. Traditional methods proposed by standards like JPEG have drawbacks, including blocking artifacts and aliasing distortions at high compression ratios.

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