A novel method to enhance medical image reconstruction using Genetic Algorithm and Incremental Principal Component Analysis - Onur, Tuğba Özge (Computers in biology and medicine, 2025)
Medical imaging has an crucial role in modern healthcare and helps diagnosing and treating for a variety of medical conditions. However, the quality of medical images can be affected by factors such as noise, artifacts, and limited resolution. This paper proposes a novel approach for enhancing the reconstruction of medical images by combining Genetic Algorithm (GA) with Incremental Principal Component Analysis (IPCA). The proposed method aims to improve image quality by extracting relevant features from the original image using GA, followed by reconstruction using IPCA. Through this comprehensive approach, the goal is to enhance the reconstruction of medical images and improve their diagnostic utility in clinical practice. To prove the validity of the proposed method, five different magnetic resonance (MR) images of the shoulder joints are used and the image quality are measured using the signal-to-noise ratio (SNR) terminology with peak signal-to-noise ratio (PSNR), a structural similarity index measure (SSIM) and contrast-to-noise ratio (CNR). The results demonstrate significant improvements in image quality, confirming the effectiveness of the proposed method in enhancing the reconstruction of medical images. •The novel approach combines Genetic Algorithm (GA) and Incremental Principal Component Analysis (IPCA) for improved medical image reconstruction.•GA extracts features, while IPCA enhances medical image reconstruction.•The proposed approach boosts medical image quality using GA and IPCA.•The results show that combining GA with IPCA effectively enhances medical image reconstruction.