Dr. Bo Chen Profile

Dr. Bo Chen

at Shenzhen Univ

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Publications (4)

Proceedings Article | 27 June 2023 Paper

A correntropy-based local additive bias-field-corrected image fitting model for image segmentation

Haoming Chen, Bo Chen, Yuru Zhang, Wensheng Chen, Yuwen Jiang, Binbin Pan

Proceedings Volume 12705, 127051J (2023) https://doi.org/10.1117/12.2680576

KEYWORDS: Image segmentation, Inhomogeneities, Bias correction, Contour modeling, Image processing

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Proceedings Article | 9 March 2018 Paper

A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images

Yongfeng Gao, Zhengrong Liang, Yuxiang Xing, Hao Zhang, Jianhua Ma, Hongbing Lu, Lihong Li, Bo Chen, Marc Pomeroy, William Moore

Proceedings Volume 10573, 105733C (2018) https://doi.org/10.1117/12.2293854

KEYWORDS: Magnetorheological finishing, Tissues, X-ray computed tomography, Lung, Image segmentation, Databases, X-rays, Reconstruction algorithms, Computed tomography, CT reconstruction

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Tremendous research efforts have been devoted to lower the X-ray radiation exposure to the patient in order to expand the utility of computed tomography (CT), particularly to pediatric imaging and population-based screening. When the exposure dosage goes down, both the X-ray quanta fluctuation and the system electronic background noise become significant factors affecting the image quality. Conventional edge-preserving noise smoothing would sacrifice tissue textures and compromise the clinical tasks. To relieve these challenges, this work models the noise problem by pre-log shifted Poisson statistics and extracts tissue textures from previous normal-dose CT scans as prior knowledge for texturepreserving Bayesian reconstruction of current ultralow-dose CT images. The pre-log shift Poisson model considers accurately both the X-ray quanta fluctuation and the system electronic noise while the prior knowledge of tissue textures removes the limitation of the conventional edge-preserving noise smoothing. The Bayesian reconstruction was tested by experimental studies. One patient chest scan was selected from a database of 133 patients’ scans at 100mAs/120kVp normal-dose level. From the selected patient scan, ultralow-dose data was simulated at 5mAs/120kVp level. The other 132 normal-dose scans were grouped according to how close their lung tissue texture patterns are from that of the selected patient scan. The tissue textures of each group were used to reconstruct the ultralow-dose scan by the Bayesian algorithm. The closest group to the selected patient produced almost identical results to the reconstruction when the tissue textures of the selected patient’s normal-dose scan were used, indicating the feasibility of extracting tissue textures from a previous normal-dose database to reconstruct any current ultralow-dose CT image. Since the Bayesian reconstruction can be time consuming, this work further investigates a strategy to efficiently store the projection matrix rather than computing the line integrals on-flight. This strategy accelerated the computing speed by more than 18 times.

Proceedings Article | 2 March 2018 Paper

A new medical image segmentation model based on fractional order differentiation and level set

Bo Chen, Shan Huang, Feifei Xie, Lihong Li, Wensheng Chen, Zhengrong Liang

Proceedings Volume 10574, 1057435 (2018) https://doi.org/10.1117/12.2292931

KEYWORDS: Image segmentation, Medical imaging, Magnetic resonance imaging, Fourier transforms, Heart, Computed tomography, Neuroimaging, Brain, Image enhancement, Visual process modeling

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Proceedings Article | 27 February 2018 Presentation + Paper

A new fractional order derivative based active contour model for colon wall segmentation

Bo Chen, Lihong Li, Huafeng Wang, Xinzhou Wei, Shan Huang, Wensheng Chen, Zhengrong Liang

Proceedings Volume 10575, 1057517 (2018) https://doi.org/10.1117/12.2293677

KEYWORDS: Colon, Image segmentation, Solid modeling, Photovoltaics, Colorectal cancer, Computer aided diagnosis and therapy, Cancer, Expectation maximization algorithms, Image processing, Tomography

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