Brain Magnetic Resonance Image Inpainting via Deep Edge Region-based Generative Adversarial Network

Abstract

Human brains are the most complex organs. There are a number of functions that this three-pound organ performs, including intelligence, interpreter of the senses, initiator of bodily movements, and controller of behaviour. In this paper, a novel ER-GAN model has been proposed for image inpainting (IIP) Brain MRI images. Initially, the brain MRI images are segmented using Attention V-Net. In the first GAN, Edge reconstruction Generative Adversarial Networks (EGAN) are used as edge generators able to hallucinate edges in missing regions based on the rest of the image’s edges and grayscale pixel intensities. Edge generation in brain MRI images involves leveraging these grayscale pixel intensities to detect boundaries between different brain tissues or structures. The varying intensities in MRI images often correspond to changes in tissue composition or boundaries between anatomical regions, making them valuable for edge detection and delineation. The second GAN uses the Region Reconstruction Generative Adversarial Network (RGAN) to fill in the missing regions by combining edge information from the missing regions and color and texture information from the surrounding regions. In experimental analysis, the Jaccard Index (JI) and Dice Index (DI) are obtained at 0.78 and 0.84 respectively. The proposed ER-GAN model reaches an overall accuracy of 99.25%, which is comparatively better than the existing techniques.