Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images

Abstract

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.