Prof. Yanhui Guo Profile

Prof. Yanhui Guo

Assistant Professor at Univ of Illinois at Springfield

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Publications (12)

Proceedings Article | 16 March 2020 Presentation + Paper

Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis

Yue Li, Zheng Xie, Zilong He, Xiangyuan Ma, Yanhui Guo, Weiguo Chen, Yao Lu

Proceedings Volume 11314, 1131417 (2020) https://doi.org/10.1117/12.2549143

KEYWORDS: Digital breast tomosynthesis, Architectural distortion, Breast cancer

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Proceedings Article | 6 May 2019 Paper

A novel region growing approach using similarity set score and homogeneity based on neutrosophic set for ultrasound image segmentation

Xue Jiang, Yanhui Guo, Yao Lu

Proceedings Volume 11069, 110692C (2019) https://doi.org/10.1117/12.2524144

KEYWORDS: Image segmentation, Tumors, Ultrasonography, Breast, Computer aided diagnosis and therapy, Image processing algorithms and systems, Computing systems

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Proceedings Article | 13 March 2019 Paper

Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network

Guanxiong Cai, Yanhui Guo, Weiguo Chen, Hui Zeng, Yuanpin Zhou, Yao Lu

Proceedings Volume 10950, 109502J (2019) https://doi.org/10.1117/12.2512355

KEYWORDS: Mammography, Breast cancer, Convolution, Neural networks, Image processing, Computer-aided diagnosis, Classification systems, Breast, Computer aided diagnosis and therapy, Image enhancement

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Proceedings Article | 8 March 2019 Paper

Three-dimensional reconstruction of internal fascicles of human peripheral nerve

Shouliang Liu, Yanhui Guo, Liwei Yan, Yao Lu

Proceedings Volume 10951, 109512G (2019) https://doi.org/10.1117/12.2512380

KEYWORDS: Nerve, Image segmentation, 3D modeling, Computed tomography, Convolution, Sun, Adhesives, Biological research, Computer science, Network architectures

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Proceedings Article | 6 July 2018 Paper

A novel nipple detection algorithm on Digital Mammography (DM)

Jiayu Jiang, Yao Lu, Yanhui Guo

Proceedings Volume 10718, 107181C (2018) https://doi.org/10.1117/12.2318042

KEYWORDS: Nipple, Breast, Mammography, Digital mammography, Detection and tracking algorithms, Chest, Error analysis, Image processing, Feature extraction

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Proceedings Article | 27 February 2018 Paper

A fully automatic microcalcification detection approach based on deep convolution neural network

Guanxiong Cai, Yanhui Guo, Yaqin Zhang, Genggeng Qin, Yuanpin Zhou, Yao Lu

Proceedings Volume 10575, 105752Q (2018) https://doi.org/10.1117/12.2293593

KEYWORDS: Neural networks, Breast cancer, Signal detection, Mammography, Computer aided diagnosis and therapy, Detection and tracking algorithms, Breast, Image processing

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Proceedings Article | 1 April 2013 Paper

Multiscale intensity homogeneity transformation method and its application to computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA)

Yanhui Guo, Chuan Zhou, Heang-Ping Chan, Jun Wei, Aamer Chughtai, Baskaran Sundaram, Lubomir Hadjiiski, Smita Patel, Ella Kazerooni

Proceedings Volume 8670, 867036 (2013) https://doi.org/10.1117/12.2008053

KEYWORDS: CAD systems, Tomography, Angiography, Computer aided design, Statistical analysis, Computer aided diagnosis and therapy, Feature selection, Radiology, Medical imaging

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Proceedings Article | 29 March 2013 Paper

Curved planar reformation and optimal path tracing (CROP) method for false positive reduction in computer-aided detection of pulmonary embolism in CTPA

Chuan Zhou, Heang-Ping Chan, Yanhui Guo, Jun Wei, Aamer Chughtai, Lubomir Hadjiiski, Baskaran Sundaram, Smita Patel, Jean Kuriakose, Ella Kazerooni

Proceedings Volume 8670, 867035 (2013) https://doi.org/10.1117/12.2008048

KEYWORDS: Feature extraction, Visualization, Image segmentation, CAD systems, Lung, Arteries, Computer aided diagnosis and therapy, Tissues, Electroluminescence

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The curved planar reformation (CPR) method re-samples the vascular structures along the vessel centerline to generate longitudinal cross-section views. The CPR technique has been commonly used in coronary CTA workstation to facilitate radiologists’ visual assessment of coronary diseases, but has not yet been used for pulmonary vessel analysis in CTPA due to the complicated tree structures and the vast network of pulmonary vasculature. In this study, a new curved planar reformation and optimal path tracing (CROP) method was developed to facilitate feature extraction and false positive (FP) reduction and improve our PE detection system. PE candidates are first identified in the segmented pulmonary vessels at prescreening. Based on Dijkstra’s algorithm, the optimal path (OP) is traced from the pulmonary trunk bifurcation point to each PE candidate. The traced vessel is then straightened and a reformatted volume is generated using CPR. Eleven new features that characterize the intensity, gradient, and topology are extracted from the PE candidate in the CPR volume and combined with the previously developed 9 features to form a new feature space for FP classification. With IRB approval, CTPA of 59 PE cases were retrospectively collected from our patient files (UM set) and 69 PE cases from the PIOPED II data set with access permission. 595 and 800 PEs were manually marked by experienced radiologists as reference standard for the UM and PIOPED set, respectively. At a test sensitivity of 80%, the average FP rate was improved from 18.9 to 11.9 FPs/case with the new method for the PIOPED set when the UM set was used for training. The FP rate was improved from 22.6 to 14.2 FPs/case for the UM set when the PIOPED set was used for training. The improvement in the free response receiver operating characteristic (FROC) curves was statistically significant (p<0.05) by JAFROC analysis, indicating that the new features extracted from the CROP method are useful for FP reduction.

SPIE Journal Paper | 7 January 2013

Modified neutrosophic approach to color image segmentation

Ebru Karabatak, Abdulkadir Sengur, Yanhui Guo

JEI, Vol. 22, Issue 01, 013005, (January 2013) https://doi.org/10.1117/12.10.1117/1.JEI.22.1.013005

KEYWORDS: Image segmentation, Image processing algorithms and systems, Color image segmentation, Color image processing, Image processing, RGB color model, Image filtering, Databases, Optical filters, Image compression

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SPIE Journal Paper | 17 September 2012

Image noise removal approach based on subpixel anisotropic diffusion

Yanhui Guo, Heng-Da Cheng

JEI, Vol. 21, Issue 3, 033026, (September 2012) https://doi.org/10.1117/12.10.1117/1.JEI.21.3.033026

KEYWORDS: Signal to noise ratio, Anisotropic diffusion, Diffusion, Image processing, Phase modulation, Speckle, Denoising, Image filtering, Smoothing, Detection and tracking algorithms

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Proceedings Article | 23 February 2012 Paper

Automatic seed point identification and main artery segmentation for pulmonary vascular tree segmentation and tracking in computed tomographic pulmonary angiography (CTPA)

Yanhui Guo, Chuan Zhou, Heang-Ping Chan, Jean Kuriakose, Aamer Chughtai, Jun Wei, Lubomir Hadjiiski, Ella Kazerooni

Proceedings Volume 8315, 83152P (2012) https://doi.org/10.1117/12.912848

KEYWORDS: Arteries, Image segmentation, Heart, Automatic tracking, Tomography, Angiography, Lung, Computer aided diagnosis and therapy, Chest

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Proceedings Article | 23 February 2012 Paper

Pulmonary vessel segmentation utilizing curved planar reformation and optimal path finding (CROP) in computed tomographic pulmonary angiography (CTPA) for CAD applications

Chuan Zhou, Heang-Ping Chan, Jean Kuriakose, Aamer Chughtai, Jun Wei, Lubomir Hadjiiski, Yanhui Guo, Smita Patel, Ella Kazerooni

Proceedings Volume 8315, 83150N (2012) https://doi.org/10.1117/12.912446

KEYWORDS: Image segmentation, Lung, Computer aided diagnosis and therapy, Tissues, Angiography, Arteries, Tomography, Lymphatic system, Matrices, Radiology

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Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately under suboptimal conditions, such as vessels occluded by PEs, surrounded by lymphoid tissues or lung diseases, and crossing with other vessels. In this study, we developed a new vessel refinement method utilizing curved planar reformation (CPR) technique combined with optimal path finding method (MHES-CROP). The MHES segmented vessels straightened in the CPR volume was refined using adaptive gray level thresholding where the local threshold was obtained from least-square estimation of a spline curve fitted to the gray levels of the vessel along the straightened volume. An optimal path finding method based on Dijkstra's algorithm was finally used to trace the correct path for the vessel of interest. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOIs) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 32.9±10.2% using the MHES method to 9.9±7.9% using the MHES-CROP method. The accuracy of vessel segmentation was improved significantly (p<0.05). The intraclass correlation coefficient (ICC) of the segmented vessel volume between the automated segmentation and the reference standard was improved from 0.919 to 0.988. Quantitative comparison of the MHES method and the MHES-CROP method with the reference standard was also evaluated by the Bland-Altman plot. This preliminary study indicates that the MHES-CROP method has the potential to improve PE detection.

Showing 5 of 12 publications

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