Enhancing Medical Image Denoising: A Hybrid Approach Incorporating Adaptive Kalman Filter and Non-Local Means with Latin Square Optimization

Medical image denoising plays a critical role in enhancing the quality of diagnostic imaging, where noise reduction without compromising image details is paramount. In this paper, we propose a novel hybrid approach aimed at improving the denoising efficacy for medical images. Initially, we employ an adaptive Kalman filter to attenuate noise, leveraging its proficiency in state estimation from noisy measurements. Unlike conventional Kalman filters with fixed parameters, our adaptive Kalman filter dynamically adjusts its parameters based on the noise characteristics of the input image, thus offering enhanced accuracy in estimating the underlying true state of the system represented by the medical image. Subsequently, both a non-local means (NLM) method and a median filter are introduced as post-processing steps to further refine the denoised image. The NLM method leverages the similarities between image patches to effectively reduce noise, while the median filter further enhances the denoised image by suppressing residual noise and preserving image details. However, the effectiveness of NLM and the median filter is highly dependent on carefully chosen parameters, which traditionally necessitates extensive computational resources for optimization. To address this challenge, we introduce the innovative use of Latin square optimization, a structured experimental design technique, to efficiently determine optimal parameters for NLM. By systematically exploring parameter combinations using Latin square optimization, we mitigate the complexity of experiments while enhancing denoising performance. The experimental results on medical images demonstrate the effectiveness of our proposed approach, showcasing significant improvements in noise reduction and the preservation of image features compared to conventional methods. Our hybrid approach not only advances the state-of-the-art in medical image denoising but also presents a practical solution for optimizing parameter selection in NLM, thereby facilitating their broader adoption in medical imaging applications.