Figure-ground segmentation based medical image denoising using deep convolutional neural networks

Abstract

Medical image noise can have a substantial impact on both diagnosis accuracy and image quality. Noise can cause problems with tasks related to diagnosis, like defining object boundaries, which are essential for precise diagnosis. By applying denoising techniques, medical imaging professionals can significantly improve image quality, reduce errors, and enhance the accuracy of diagnoses and treatments. This article presents a method for reducing noises like Gaussian noise, salt and pepper noise, speckle noise, and ring artefacts in medical images, such as magnetic resonance imaging (MRI), computed tomography (CT), and chest X-ray images. This study explores the integration of adaptive CNNs with guided image filtering for enhanced image quality and deep learning-driven Figure-Ground segmentation. The proposed method's performance is extensively evaluated on chest X-ray and MRI/CT images under diverse noise scenarios, with comparisons to existing techniques using established statistical metrics. The results validate that the proposed approach attains superior performance, yielding the highest values across these metrics.