title: Echocardiogram enhancement using supervised manifold denoising.
abstract: This paper presents data-driven methods for echocardiogram enhancement. Existing denoising algorithms typically rely on a single noise model, and do not generalize to the composite noise sources typically found in real-world echocardiograms. Our methods leverage the low-dimensional intrinsic structure of echocardiogram videos. We assume that echocardiogram images are noisy samples from an underlying manifold parametrized by cardiac motion and denoise images via back-projection onto a learned (non-linear) manifold. Our methods incorporate synchronized side information (e.g., electrocardiography), which is often collected alongside the visual data. We evaluate the proposed methods on a synthetic data set and real-world echocardiograms. Quantitative results show improved performance of our methods over recent image despeckling methods and video denoising methods, and a visual analysis of real-world data shows noticeable image enhancement, even in the challenging case of noise due to dropout artifacts.
authors.affiliation: Department of Computer Science, University of North Carolina at Charlotte. Electronic address: hwu13@uncc.edu.
authors.forename: Hui
authors.initials: H
authors.lastname: Wu
authors.affiliation: Department of General Surgery, Carolinas Medical Center.
authors.forename: Toan T
authors.initials: TT
authors.lastname: Huynh
authors.affiliation: Department of Computer Science, University of North Carolina at Charlotte.
authors.forename: Richard
authors.initials: R
authors.lastname: Souvenir
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004452:Echocardiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D000069553:Supervised Machine Learning
keywords: Biomedical image processing; Echocardiography; Image denoising; Supervised learning; Video signal processing