Dr. Jun Ge Profile

Dr. Jun Ge

at Hologic Inc

SPIE Involvement:

Author

Publications (18)

Proceedings Article | 19 March 2008 Paper

Digital tomosynthesis mammography: improvement of artifact reduction method for high-attenuation objects on reconstructed slices

Jun Ge, Heang-Ping Chan, Berkman Sahiner, Yiheng Zhang, Jun Wei, Lubomir Hadjiiski, Chuan Zhou, Yi-Ta Wu, Jiazheng Shi

Proceedings Volume 6913, 69134O (2008) https://doi.org/10.1117/12.771539

KEYWORDS: Digital mammography, Imaging systems, Modulation transfer functions, Sensors, X-ray sources, X-rays, Breast, Data modeling, Visualization, X-ray imaging

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Proceedings Article | 18 March 2008 Paper

Truncation artifact and boundary artifact reduction in breast tomosynthesis reconstruction

Yiheng Zhang, Heang-Ping Chan, Yi-Ta Wu, Berkman Sahiner, Chuan Zhou, Jun Wei, Jun Ge, Lubomir Hadjiiski, Jiazheng Shi

Proceedings Volume 6913, 69132Y (2008) https://doi.org/10.1117/12.769698

KEYWORDS: Breast, Photovoltaics, Sensors, Image quality, Digital mammography, Tissues, Reconstruction algorithms, Image processing, Signal attenuation, Mammography

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Proceedings Article | 17 March 2008 Paper

Automated detection of breast vascular calcification on full-field digital mammograms

Jun Ge, Heang-Ping Chan, Berkman Sahiner, Chuan Zhou, Mark Helvie, Jun Wei, Lubomir Hadjiiski, Yiheng Zhang, Yi-Ta Wu, Jiazheng Shi

Proceedings Volume 6915, 691517 (2008) https://doi.org/10.1117/12.773096

KEYWORDS: Image segmentation, Computer aided diagnosis and therapy, Mammography, Breast, Algorithm development, Blood vessels, Feature selection, Computing systems, Arteries

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Breast vascular calcifications (BVCs) are calcifications that line the blood vessel walls in the breast and appear as parallel or tubular tracks on mammograms. BVC is one of the major causes of the false positive (FP) marks from computer-aided detection (CADe) systems for screening mammography. With the detection of BVCs and the calcified vessels identified, these FP clusters can be excluded. Moreover, recent studies reported the increasing interests in the correlation between mammographically visible BVCs and the risk of coronary artery diseases. In this study, we developed an automated BVC detection method based on microcalcification prescreening and a new k-segments clustering algorithm. The mammogram is first processed with a difference-image filtering technique designed to enhance calcifications. The calcification candidates are selected by an iterative process that combines global thresholding and local thresholding. A new k-segments clustering algorithm is then used to find a set of line segments that may be caused by the presence of calcified vessels. A linear discriminant analysis (LDA) classifier was designed to reduce false segments that are not associated with BVCs. Four features for each segment selected with stepwise feature selection were used for this LDA classification. Finally, the neighboring segments were linked and dilated with morphological dilation to cover the regions of calcified vessels. A data set of 16 FFDM cases with vascular calcifications was collected for this preliminary study. Our preliminary result demonstrated that breast vascular calcifications can be accurately detected and the calcified vessels identified. It was found that the automated method can achieve a detection sensitivity of 65%, 70%, and 75% at 6.1 mm, 8.4 mm, and 12.6mm FP segments/image, respectively, without any true clustered microcalcifications being falsely marked. Further work is underway to improve this method and to incorporate it into our FFDM CADe system.

Proceedings Article | 17 March 2008 Paper

Comparison of mammographic parenchymal patterns of normal subjects and breast cancer patients

Yi-Ta Wu, Berkman Sahiner, Heang-Ping Chan, Jun Wei, Lubomir Hadjiiski, Mark Helvie, Yiheng Zhang, Jiazheng Shi, Chuan Zhou, Jun Ge, Jing Cui

Proceedings Volume 6915, 691520 (2008) https://doi.org/10.1117/12.771278

KEYWORDS: Breast, Mammography, Breast cancer, Cancer, Image segmentation, Received signal strength, Feature extraction, Statistical analysis, Computer aided diagnosis and therapy, Computing systems

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Proceedings Article | 17 March 2008 Paper

Breast mass segmentation on dynamic contrast-enhanced magnetic resonance scans using the level set method

Jiazheng Shi, Berkman Sahiner, Heang-Ping Chan, Chintana Paramagul, Lubomir Hadjiiski, Mark Helvie, Yi-Ta Wu, Jun Ge, Yiheng Zhang, Chuan Zhou, Jun Wei

Proceedings Volume 6915, 69152A (2008) https://doi.org/10.1117/12.771390

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, 3D image processing, Magnetism, Breast cancer, 3D acquisition, 3D scanning, 3D modeling, Mathematical modeling

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Proceedings Article | 17 March 2008 Paper

Classification of breast masses and normal tissues in digital tomosynthesis mammography

Jun Wei, Heang-Ping Chan, Yiheng Zhang, Berkman Sahiner, Chuan Zhou, Jun Ge, Yi-Ta Wu, Lubomir Hadjiiski

Proceedings Volume 6915, 691508 (2008) https://doi.org/10.1117/12.771189

KEYWORDS: Breast, Digital mammography, Tissues, Statistical analysis, Computer aided diagnosis and therapy, Reconstruction algorithms, Feature extraction, Breast cancer, Photovoltaics, Image segmentation

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Proceedings Article | 17 March 2008 Paper

Automated segmentation and tracking of coronary arteries in ECG-gated cardiac CT scans

Chuan Zhou, Heang-Ping Chan, Aamer Chughtai, Smita Patel, Prachi Agarwal, Lubomir Hadjiiski, Berkman Sahiner, Jun Wei, Jun Ge, Ella Kazerooni

Proceedings Volume 6915, 69150O (2008) https://doi.org/10.1117/12.770362

KEYWORDS: Arteries, Image segmentation, Heart, Computed tomography, Optical spheres, Visualization, Optical tracking, Expectation maximization algorithms, Blood, Image enhancement

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Proceedings Article | 30 March 2007 Paper

Automated detection of pulmonary embolism (PE) in computed tomographic pulmonary angiographic (CTPA) images: multiscale hierachical expectation-maximization segmentation of vessels and PEs

Chuan Zhou, Heang-Ping Chan, Lubomir Hadjiiski, Aamer Chughtai, Smita Patel, Philip Cascade, Berkman Sahiner, Jun Wei, Jun Ge, Ella Kazerooni

Proceedings Volume 6514, 65142F (2007) https://doi.org/10.1117/12.713769

KEYWORDS: Image segmentation, Arteries, Lung, Tissues, 3D image enhancement, Angiography, Computer aided diagnosis and therapy, 3D image processing, Chest, Expectation maximization algorithms

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Proceedings Article | 29 March 2007 Paper

Computer-aided detection of breast masses on prior mammograms

Jun Wei, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Marilyn Roubidoux, Mark Helvie, Jun Ge, Chuan Zhou, Yi-Ta Wu

Proceedings Volume 6514, 651405 (2007) https://doi.org/10.1117/12.713768

KEYWORDS: Mammography, CAD systems, Computer aided design, Statistical analysis, Cancer, Computer aided diagnosis and therapy, Visibility, Breast, Feature extraction, Feature selection

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Proceedings Article | 15 March 2007 Paper

Investigation of the Z-axis resolution of breast tomosynthesis mammography systems

Yiheng Zhang, Heang-Ping Chan, Berkman Sahiner, Jun Wei, Jun Ge, Lubomir Hadjiiski, Chuan Zhou

Proceedings Volume 6510, 65104A (2007) https://doi.org/10.1117/12.713816

KEYWORDS: Breast, Mammography, Reconstruction algorithms, Imaging systems, Digital mammography, Photovoltaics, Image resolution, Computer simulations, Image acquisition, Breast cancer

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Proceedings Article | 1 March 2007 Paper

Digital tomosynthesis mammography: intra- and interplane artifact reduction for high-contrast objects on reconstructed slices using a priori 3D geometrical information

Jun Ge, Heang-Ping Chan, Berkman Sahiner, Yiheng Zhang, Jun Wei, Lubomir Hadjiiski, Chuan Zhou

Proceedings Volume 6512, 65124Q (2007) https://doi.org/10.1117/12.708914

KEYWORDS: Digital mammography, Breast, Reconstruction algorithms, Signal to noise ratio, 3D image processing, Sensors, Metals, Iterative methods, Prototyping, Mammography

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We are developing a computerized technique to reduce intra- and interplane ghosting artifacts caused by high-contrast objects such as dense microcalcifications (MCs) or metal markers on the reconstructed slices of digital tomosynthesis mammography (DTM). In this study, we designed a constrained iterative artifact reduction method based on a priori 3D information of individual MCs. We first segmented individual MCs on projection views (PVs) using an automated MC detection system. The centroid and the contrast profile of the individual MCs in the 3D breast volume were estimated from the backprojection of the segmented individual MCs on high-resolution (0.1 mm isotropic voxel size) reconstructed DTM slices. An isolated volume of interest (VOI) containing one or a few MCs is then modeled as a high-contrast object embedded in a local homogeneous background. A shift-variant 3D impulse response matrix (IRM) of the projection-reconstruction (PR) system for the extracted VOI was calculated using the DTM geometry and the reconstruction algorithm. The PR system for this VOI is characterized by a system of linear equations. A constrained iterative method was used to solve these equations for the effective linear attenuation coefficients (eLACs) within the isolated VOI. Spatial constraint and positivity constraint were used in this method. Finally, the intra- and interplane artifacts on the whole breast volume resulting from the MC were calculated using the corresponding impulse responses and subsequently subtracted from the original reconstructed slices. The performance of our artifact-reduction method was evaluated using a computer-simulated MC phantom, as well as phantom images and patient DTMs obtained with IRB approval. A GE prototype DTM system that acquires 21 PVs in 3º increments over a ±30º range was used for image acquisition in this study. For the computer-simulated MC phantom, the eLACs can be estimated accurately, thus the interplane artifacts were effectively removed. For MCs in phantom and patient DTMs, our method reduced the artifacts but also created small over-corrected areas in some cases. Potential reasons for this may include: the simplified mathematical modeling of the forward projection process, and the amplified noise in the solution of the system of linear equations.

Proceedings Article | 17 March 2006 Paper

Computer-aided detection of clustered microcalcifications on full-field digital mammograms: a two-view information fusion scheme for FP reduction

Jun Ge, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Mark Helvie, Chuan Zhou, Jun Wei, Yiheng Zhang

Proceedings Volume 6144, 61445R (2006) https://doi.org/10.1117/12.654997

KEYWORDS: Mammography, CAD systems, Information fusion, Breast, Computer aided design, Image segmentation, Feature extraction, Computer aided diagnosis and therapy, Signal to noise ratio

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We are developing new techniques to improve the performance of our computer-aided detection (CAD) system for clustered microcalcifications on full-field digital mammograms (FFDMs). In this study, we designed an information fusion scheme by using joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their geometrical information. Candidate pairs were classified as true and false pairs with a similarity classifier that used the joint information from both views. Each cluster candidate was also characterized by its single-view features. The outputs of the similarity classifier and the single-view classifier were fused and the cluster candidate was classified as a true microcalcification cluster or a false-positive (FP) using the fused two-view information. A data set of 192 FFDM images was collected from 96 patients at the University of Michigan. All patients had two mammographic views. This data set contained 96 microcalcification clusters, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. For training and testing the classifiers, the data set was partitioned into two independent subsets with the malignant cases equally distributed to the two subsets. One subset was used for training and the other subset was used for testing. We compared three computerized methods for geometrically pairing cluster candidates on two mammographic views. The areas under the fitted ROC curves were 0.75±0.01, 0.74±0.01, and 0.76±0.01 for the three methods, respectively. The difference between any two methods measured by the area under the fitted ROC curve, A_z, was not statistically significant (p > 0.05). We also evaluated a new hybrid pairing scheme that used two different sensitivity levels for defining cluster pairs based on the single-view scores. The single-view CAD system achieved cluster-based sensitivities of 75%, 80%, and 85% at 0.48, 0.86, and 1.05 FPs/image, respectively. The joint two-view CAD system achieved the same sensitivity levels at 0.29, 0.46, and 0.89 FPs/image. When the hybrid pairing was used in the joint two-view CAD system, the same cluster-based sensitivities were achieved at 0.26, 0.37, and 0.88 FPs/image. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters.

Proceedings Article | 17 March 2006 Paper

Regularized discriminate analysis for breast mass detection on full field digital mammograms

Jun Wei, Berkman Sahiner, Yiheng Zhang, Heang-Ping Chan, Lubomir Hadjiiski, Chuan Zhou, Jun Ge, Yi-Ta Wu

Proceedings Volume 6144, 61445P (2006) https://doi.org/10.1117/12.654897

KEYWORDS: Feature selection, Feature extraction, Mammography, CAD systems, Breast, Computer aided design, Tissues, Computer aided diagnosis and therapy, Classification systems

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In computer-aided detection (CAD) applications, an important step is to design a classifier for the differentiation of the abnormal from the normal structures. We have previously developed a stepwise linear discriminant analysis (LDA) method with simplex optimization for this purpose. In this study, our goal was to investigate the performance of a regularized discriminant analysis (RDA) classifier in combination with a feature selection method for classification of the masses and normal tissues detected on full field digital mammograms (FFDM). The feature selection scheme combined a forward stepwise feature selection process and a backward stepwise feature elimination process to obtain the best feature subset. An RDA classifier and an LDA classifier in combination with this new feature selection method were compared to an LDA classifier with stepwise feature selection. A data set of 130 patients containing 260 mammograms with 130 biopsy-proven masses was used. All cases had two mammographic views. The true locations of the masses were identified by experienced radiologists. To evaluate the performance of the classifiers, we randomly divided the data set into two independent sets of approximately equal size for training and testing. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained by averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 80% and 70% on the test set, our RDA classifier with the new feature selection scheme achieved an FP rate of 1.8, 1.1, and 0.6 FPs/image, respectively, compared to 2.1, 1.4, and 0.8 FPs/image with stepwise LDA with simplex optimization. Our results indicate that RDA in combination with the sequential forward inclusion-backward elimination feature selection method can improve the performance of mass detection on mammograms. Further work is underway to optimize the feature selection and classification scheme and to evaluate if this approach can be generalized to other CAD classification tasks.

Proceedings Article | 15 March 2006 Paper

Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images

Chuan Zhou, Heang-Ping Chan, Lubomir Hadjiiski, Smita Patel, Philip Cascade, Berkman Sahiner, Jun Wei, Jun Ge, Ella Kazerooni

Proceedings Volume 6144, 61444Q (2006) https://doi.org/10.1117/12.655343

KEYWORDS: Image segmentation, Lung, Arteries, 3D image processing, Gold, Tissues, Tomography, Angiography, Lymphatic system, Computer aided diagnosis and therapy

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Automatic and accurate segmentation of the pulmonary vessels in 3D computed tomographic angiographic images (CTPA) is an essential step for computerized detection of pulmonary embolism (PE) because PEs only occur inside the pulmonary arteries. We are developing an automated method to segment the pulmonary vessels in 3D CTPA images. The lung region is first extracted using thresholding and morphological operations. 3D multiscale filters in combination with a newly developed response function derived from the eigenvalues of Hessian matrices are used to enhance all vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. At each scale, a volume of interest (VOI) containing the response function value at each voxel is defined. The voxels with a high response indicate that there is an enhanced vessel whose size matches the given filter scale. A hierarchical expectation-maximization (EM) estimation is then applied to the VOI to segment the vessel by extracting the high response voxels at this single scale. The vessel tree is finally reconstructed by combining the segmented vessels at all scales based on a "connected component" analysis. Two experienced thoracic radiologists provided the gold standard of pulmonary arteries by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. Two CTPA cases containing PEs were used to evaluate the performance. One of these two cases also contained other lung diseases. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The result shows that 97.3% (1868/1920) and 92.0% (2277/2476) of the manually marked center points overlapped with the segmented vessels for the cases without and with other lung disease, respectively. The results demonstrate that vessel segmentation using our method is not degraded by PE occlusion and the vessels can be accurately extracted.

Proceedings Article | 10 March 2006 Paper

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

Jun Wei, Berkman Sahiner, Lubomir Hadjiiski, Heang-Ping Chan, Mark Helvie, Marilyn Roubidoux, Chuan Zhou, Jun Ge, Yiheng Zhang

Proceedings Volume 6144, 614424 (2006) https://doi.org/10.1117/12.654593

KEYWORDS: CAD systems, Mammography, Information fusion, Computer aided design, Breast, Image fusion, Nipple, Computer aided diagnosis and therapy, Receivers

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Proceedings Article | 2 March 2006 Paper

Tomosynthesis reconstruction using the simultaneous algebraic reconstruction technique (SART) on breast phantom data

Yiheng Zhang, Heang-Ping Chan, Berkman Sahiner, Jun Wei, Mitchell Goodsitt, Lubomir Hadjiiski, Jun Ge, Chuan Zhou

Proceedings Volume 6142, 614249 (2006) https://doi.org/10.1117/12.654891

KEYWORDS: Breast, Reconstruction algorithms, Digital mammography, Tissues, Mammography, Sensors, Image quality, Breast cancer, Prototyping, 3D image processing

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Proceedings Article | 29 April 2005 Paper

Computer-aided detection of microcalcification clusters on full-field digital mammograms: multiscale pyramid enhancement and false positive reduction using an artificial neural network

Jun Ge, Jun Wei, Lubomir Hadjiiski, Berkman Sahiner, Heang-Ping Chan, Mark Helvie, Chuan Zhou

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595753

KEYWORDS: Image enhancement, CAD systems, Mammography, Image filtering, Image segmentation, Nonlinear filtering, Artificial neural networks, Computer aided diagnosis and therapy, Breast cancer

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We are developing a computer-aided detection (CAD) system to detect microcalcification clusters automatically on full field digital mammograms (FFDMs). The CAD system includes five stages: preprocessing, image enhancement and/or box-rim filtering, segmentation of microcalcification candidates, false positive (FP) reduction, and clustering. In this study, we investigated the performance of a nonlinear multiscale Laplacian pyramid enhancement method in comparison with a box-rim filter at the image enhancement stage and the use of a new error metric to improve the efficiency and robustness of the training of a convolution neural network (CNN) at the FP reduction stage of our CAD system. A data set of 96 cases with 200 images was collected at the University of Michigan. This data set contained 215 microcalcification clusters, of which 64 clusters were proven by biopsy to be malignant and 151 were proven to be benign. The data set was separated into two independent data sets. One data set was used to train and validate the CNN in our CAD system. The other data set was used to evaluate the detection performance. For this data set, Laplacian pyramid multiscale enhancement did not improve the performance of the microcalcification detection system in comparison with our box-rim filter previously optimized for digitized screen-film mammograms. With the new error metric, the training of CNN could be accelerated and the classification performance in validation was improved from an A_z value of 0.94 to 0.97 on average. The CNN in combination with rule-based classifiers could reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic (FROC) methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70%, 80%, and 88% at 0.23, 0.39, and 0.71 FP marks/image, respectively. For case-based performance evaluation, a sensitivity of 80%, 90%, and 98% can be achieved at 0.17, 0.27, and 0.51 FP marks/image, respectively.

Proceedings Article | 29 April 2005 Paper

Computer-aided detection of breast masses on mammograms: performance improvement using a dual system

Jun Wei, Berkman Sahiner, Lubomir Hadjiiski, Heang-Ping Chan, Mark Helvie, Marilyn Roubidoux, Nicholas Petrick, Jun Ge, Chuan Zhou

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595745

KEYWORDS: Mammography, CAD systems, Breast, Computing systems, Classification systems, Breast cancer, Computer aided diagnosis and therapy, Neural networks, Tissues

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