Lou, Yifei
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/4794
Dr. Yifei Lou is an Associate Professor of Mathematical Sciences. Her research interests include:
- Compressive sensing and its applications
- Image analysis (medical imaging, hyperspectral, imaging through turbulence)
- Numerical analysis and optimization algorithms
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Browsing Lou, Yifei by Subject "Image processing"
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Item Multienergy Cone-Beam Computed Tomography Reconstruction with a Spatial Spectral Nonlocal Means Algorithm(Society for Industrial and Applied Mathematics Publications) Li, B.; Shen, C.; Chi, Y.; Yang, M.; Lou, Yifei; Zhou, L.; Jia, X.; 0000-0003-1973-5704 (Lou, Y); Lou, YifeiMultienergy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multienergy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral nonlocal means (NLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type and NLM reconstruction methods. © 2018 Society for Industrial and Applied Mathematics.Item A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing(Society for Industrial and Applied Mathematics Publications, 2015-09-10) Lou, Yifei; Zeng, T.; Osher, S.; Xin, J.; 0000-0003-1973-5704 (Lou, Y)We propose a weighted difference of anisotropic and isotropic total variation (TV) as a regularization for image processing tasks, based on the well-known TV model and natural image statistics. Due to the form of our model, it is natural to compute via a difference of convex algorithm (DCA). We draw its connection to the Bregman iteration for convex problems and prove that the iteration generated from our algorithm converges to a stationary point with the objective function values decreasing monotonically. A stopping strategy based on the stable oscillatory pattern of the iteration error from the ground truth is introduced. In numerical experiments on image denoising, image deblurring, and magnetic resonance imaging (MRI) reconstruction, our method improves on the classical TV model consistently and is on par with representative state-of-the-art methods.