Browsing by Author "Cao, Shengting"

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    Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation
    (IEEE, 2021) Liu, Hongshan; Cao, Shengting; Ling, Yuye; Gan, Yu; University of Alabama Tuscaloosa; Shanghai Jiao Tong University
    Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler's elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.
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    Intelligent Treadmill Control and Holographic Rendering for Accessible Rehabilitation
    (University of Alabama Libraries, 2025) Cao, Shengting; Hu, Fei
    There is a growing need in the medical rehabilitation market due to the increasing elderly population. More than half of the patients are outpatients who require commuting from home to nursing facilities. However, the geographic distribution of these facilities is highly imbalanced, with states like Texas and California housing the largest number, while many rural and remote areas face a shortage. This disparity creates a significant burden for patients who require continuous rehabilitation but struggle with long commutes. Addressing this gap in rehabilitation accessibility is the central focus of this dissertation. This work approaches the problem from two complementary directions: (1) reducing the cost of home-based rehabilitation equipment, and (2) advancing telerehabilitation technologies. The first part of the dissertation introduces an intelligent treadmill control system that enables a single-belt treadmill to function like a split-belt treadmill, thereby providing a cost-effective solution for post-stroke gait rehabilitation at home. This system integrates real-time gait classification models and adaptive speed control to simulate split-belt dynamics without requiring specialized hardware. The second part explores novel telerehabilitation solutions. We adopted advanced neural rendering techniques to build a low-cost 3D patient reconstruction pipeline to enhance remote patient monitoring and engagement. Through these innovations, this dissertation contributes to making rehabilitation more accessible, affordable, and effective, particularly for patients in underserved areas. The proposed solutions aim to bridge the gap between clinical rehabilitation and home-based care, ultimately improving patient outcomes and reducing healthcare disparities.