Dr. Zhijin Li Profile

Dr. Zhijin Li

Advanced Application Engineer at GE Healthcare France

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Publications (7)

Proceedings Article | 13 July 2022 Paper

Lesion detection in contrast enhanced spectral mammography

Clément Jailin, Pablo Milioni, Zhijin Li, Răzvan Iordache, Serge Muller

Proceedings Volume 12286, 122860A (2022) https://doi.org/10.1117/12.2624577

KEYWORDS: Breast, Diagnostics, Data modeling, Mammography, Computer aided diagnosis and therapy, Cancer, Image classification

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Proceedings Article | 22 May 2020 Paper

Full-size anthropomorphic phantom for 2D and 3D breast x-ray imaging

James Mainprize, Gordon Mawdsley, Ann-Katherine Carton, Zhijin Li, Remy Klausz, Serge Muller, Martin Yaffe

Proceedings Volume 11513, 1151302 (2020) https://doi.org/10.1117/12.2560358

KEYWORDS: Breast, Tissues, Mammography, Signal attenuation, X-rays, 3D image processing, X-ray imaging, Image processing, Computed tomography, Laser sintering

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Proceedings Article | 9 March 2018 Presentation + Paper

Development of a physical 3D anthropomorphic breast texture model using selective laser sintering rapid prototype printing

James Mainprize, Ann-Katherine Carton, Remy Klausz, Zhijin Li, David Hunter, Gordon Mawdsley, Serge Muller, Martin Yaffe

Proceedings Volume 10573, 105730A (2018) https://doi.org/10.1117/12.2293560

KEYWORDS: Breast, 3D modeling, Printing, Tissues, Signal attenuation, Image segmentation, Laser sintering, Mammography, Digital breast tomosynthesis

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Proceedings Article | 9 March 2018 Paper

Simulation of breast compression using a new biomechanical model

Anna Mîra, Yohan Payan, Ann-Katherine Carton, Pablo Milioni de Carvalho, Zhijin Li, Viviane Devauges, Serge Muller

Proceedings Volume 10573, 105735A (2018) https://doi.org/10.1117/12.2293488

KEYWORDS: Breast, Image quality, Mammography, 3D modeling

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Proceedings Article | 7 March 2018 Presentation + Paper

Comparison of microcalcification detectability in FFDM and DBT using a virtual clinical trial

Zhijin Li, Agnès Desolneux, Serge Muller, Pablo Milioni de Carvalho, Ann-Katherine Carton

Proceedings Volume 10577, 105770D (2018) https://doi.org/10.1117/12.2293619

KEYWORDS: Digital breast tomosynthesis, Signal attenuation, Breast, Image processing, Clinical trials, X-ray imaging, X-rays, Data modeling, Image acquisition, 3D image processing

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Proceedings Article | 17 March 2015 Paper

Assessment of mass detection performance in contrast enhanced digital mammography

Ann-Katherine Carton, Pablo de Carvalho, Zhijin Li, Clarisse Dromain, Serge Muller

Proceedings Volume 9416, 941608 (2015) https://doi.org/10.1117/12.2081772

KEYWORDS: Breast, Iodine, Performance modeling, Tissues, Computer simulations, Mammography, Mathematical modeling, Digital mammography, Magnesium, Medicine

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Proceedings Article | 17 March 2015 Paper

The effect of NPS calculation method on power-law coefficient estimation accuracy in breast texture modeling

Zhijin Li, Ann-Katherine Carton, Serge Muller, Răzvan Iordache, Agnès Desolneux

Proceedings Volume 9416, 94161I (2015) https://doi.org/10.1117/12.2081764

KEYWORDS: Breast, X-ray imaging, Error analysis, 3D image processing, Mathematical modeling, Image analysis, Numerical simulations, Digital breast tomosynthesis, Image processing, Convolution

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In breast X-ray imaging, breast texture has been characterized by a radial noise power spectrum (NPS) that has an inverse power-law shape with exponent β. The technique to estimate the radial power-law coefficient β is typically based on averaging 2-dimensional noise power spectra (NPS), calculated from partly overlapping image regions each weighted by a suitable window function. The linear regression applied over a selected frequency range to the logarithm of the 1- dimensional NPS as a function of the logarithm of the radial frequencies, gives β. For each step in this process, several alternative techniques have been proposed. This paper investigates the effect of image region of interest (ROI) size, image data windowing and alternative ways to determine radial frequency in terms of bias, variance and root mean square error (RMSE) in the estimated β. The effects of these three factors were analytically derived and evaluated using synthetic images with known β varying from 1 to 4 to cover the range of textures encountered in 2D and 3D breast X-ray imaging. Our results indicate that the RMSE in estimated β is smallest when the ROIs are multiplied with an appropriate window function and either no radial averaging or radial averaging with small frequency bins is applied. The ROI size yielding the smallest RMSE depends on several factors and needs to be validated with numerical simulations. In clinical practice however, there might be a need to compromise in the choice of the ROI size to balance between the RMSE magnitudes inherent to the applied β estimation technique and encompass the breast texture range so as to obtain an accurate shape of the NPS. When using 2.56 cm x 2.56 cm ROI sizes, applying a 2D Hann window and no radial frequency averaging, the RMSE in the estimated β ranges from 0.04 to 0.1 for true β values equal to 1 and 4. While many subtleties in real images were not modeled to simplify the mathematics in deriving our results, this work is illustrative in demonstrating the limits of commonly used algorithm steps to estimate accurate β values.

Showing 5 of 7 publications

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