Dr. Brad M. Keller Profile

Dr. Brad M. Keller

at Univ of Pennsylvania Health System

SPIE Involvement:

Author

Publications (20)

Proceedings Article | 24 March 2016 Paper

Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

Shonket Ray, Brad Keller, Jinbo Chen, Emily Conant, Despina Kontos

Proceedings Volume 9785, 97851Y (2016) https://doi.org/10.1117/12.2216856

KEYWORDS: Feature extraction, Digital mammography, Breast cancer, Mammography, Feature selection, Image segmentation, Tissues, Digital imaging, Binary data

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Proceedings Article | 24 March 2016 Paper

Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case-control study

Aimilia Gastounioti, Brad Keller, Meng-Kang Hsieh, Emily Conant, Despina Kontos

Proceedings Volume 9785, 97850M (2016) https://doi.org/10.1117/12.2217697

KEYWORDS: Breast, Breast cancer, Digital mammography, Mammography, Feature extraction, Volume rendering, Image segmentation, Nipple

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Proceedings Article | 23 March 2016 Paper

Preliminary evaluation of a fully automated quantitative framework for characterizing general breast tissue histology via color histogram and color texture analysis

Brad Keller, Aimilia Gastounioti, Rebecca Batiste, Despina Kontos, Michael Feldman

Proceedings Volume 9791, 97910A (2016) https://doi.org/10.1117/12.2217094

KEYWORDS: Tissues, Feature extraction, Breast, Visualization, Collagen, Image segmentation, Tissue optics, Image analysis, Breast cancer, Pathology, Cancer

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SPIE Journal Paper | 3 April 2015

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

Brad Keller, Andrew Oustimov, Yan Wang, Jinbo Chen, Raymond Acciavatti, Yuanjie Zheng, Shonket Ray, James Gee, Andrew D. Maidment, Despina Kontos

JMI, Vol. 2, Issue 02, 024501, (April 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.2.024501

KEYWORDS: Lawrencium, Digital mammography, Breast, Imaging systems, Breast cancer, Sensors, Computing systems, Mammography, Tissues, Image segmentation

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Proceedings Article | 20 March 2015 Paper

A comparative analysis of 2D and 3D CAD for calcifications in digital breast tomosynthesis

Raymond Acciavatti, Shonket Ray, Brad Keller, Andrew D. Maidment, Emily Conant

Proceedings Volume 9414, 94140N (2015) https://doi.org/10.1117/12.2082082

KEYWORDS: Computer aided design, Breast, Biopsy, 3D image processing, Cancer, Computer aided diagnosis and therapy, Digital breast tomosynthesis, 3D vision, Algorithm development, Prototyping

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Proceedings Article | 20 March 2015 Paper

A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

Ning Yu, Jia Wu, Susan Weinstein, Bilwaj Gaonkar, Brad Keller, Ahmed Ashraf, YunQing Jiang, Christos Davatzikos, Emily Conant, Despina Kontos

Proceedings Volume 9414, 94140O (2015) https://doi.org/10.1117/12.2081943

KEYWORDS: Image segmentation, Tumors, Breast, Magnetic resonance imaging, Feature extraction, Tumor growth modeling, Medical imaging, Radiology, Cancer, Reconstruction algorithms

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Proceedings Article | 20 March 2015 Paper

Quantification of tumor changes during neoadjuvant chemotherapy with longitudinal breast DCE-MRI registration

Jia Wu, Yangming Ou, Susan Weinstein, Emily Conant, Ning Yu, Vahid Hoshmand, Brad Keller, Ahmed Ashraf, Mark Rosen, Angela DeMichele, Christos Davatzikos, Despina Kontos

Proceedings Volume 9414, 94141Z (2015) https://doi.org/10.1117/12.2081938

KEYWORDS: Tumors, Breast, Image registration, Tissues, Breast cancer, Magnetic resonance imaging, Cancer, Medical imaging, Oncology, Brain mapping

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Imaging plays a central role in the evaluation of breast tumor response to neoadjuvant chemotherapy. Image-based assessment of tumor change via deformable registration is a powerful, quantitative method potentially to explore novel information of tumor heterogeneity, structure, function, and treatment response. In this study, we continued a previous pilot study to further validate the feasibility of an open source deformable registration algorithm DRAMMS developed within our group as a means to analyze spatio-temporal tumor changes for a set of 14 patients with DCE-MR imaging. Two experienced breast imaging radiologists marked landmarks according to their anatomical meaning on image sets acquired before and during chemotherapy. Yet, chemotherapy remarkably changed the anatomical structure of both tumor and normal breast tissue, leading to significant discrepancies between both raters for landmarks in certain areas. Therefore, we proposed a novel method to grade the manually denoted landmarks into different challenge levels based on the inter-rater agreement, where a high level indicates significant discrepancies and considerable amounts of anatomical structure changes, which would indeed impose giant problem for the following registration algorithm. It is interesting to observe that DRAMMS performed in a similar manner as the human raters: landmark errors increased as inter-rater differences rose. Among all selected six deformable registration algorithms, DRAMMS achieves the highest overall accuracy, which is around 5.5 mm, while the average difference between human raters is 3 mm. Moreover, DRAMMS performed consistently well within both tumor and normal tissue regions. Lastly, we comprehensively tuned the fundamental parameters of DRAMMS to better understand DRAMMS to guide similar works in the future. Overall, we further validated that DRAMMS is a powerful registration tool to accurately quantify tumor changes and potentially predict early tumor response to chemotherapy. Therefore, future studies that aim at examining if DRAMMS can generate valuable biomarkers for tumor response prediction during chemotherapy become feasible.

Proceedings Article | 27 March 2014 Paper

Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer

Brad Keller, Jinbo Chen, Emily Conant, Despina Kontos

Proceedings Volume 9035, 90351D (2014) https://doi.org/10.1117/12.2043710

KEYWORDS: Breast, Breast cancer, Tissues, Cancer, Mammography, Statistical modeling, Performance modeling, Receptors, Receivers, Tumor growth modeling

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Proceedings Article | 20 March 2014 Paper

Application of computer-extracted breast tissue texture features in predicting false-positive recalls from screening mammography

Shonket Ray, Jae Choi, Brad Keller, Jinbo Chen, Emily Conant, Despina Kontos

Proceedings Volume 9035, 90351X (2014) https://doi.org/10.1117/12.2043742

KEYWORDS: Tissues, Breast, Biopsy, Mammography, Breast cancer, Image segmentation, Biological research, Tumor growth modeling, Cancer, Digital mammography

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Proceedings Article | 19 March 2013 Paper

Testing realism of software breast phantoms: texture analysis of synthetic mammograms

Predrag Bakic, Brad Keller, Yuanjie Zheng, Yan Wang, James Gee, Despina Kontos, Andrew Maidment

Proceedings Volume 8668, 866824 (2013) https://doi.org/10.1117/12.2008173

KEYWORDS: Breast, Mammography, Tissues, Software development, Breast imaging, Image analysis, Imaging systems, X-rays, Computer simulations, Ions

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Proceedings Article | 6 March 2013 Paper

Dependence of radiation dose on area and volumetric mammographic breast density estimation

H. Jing, B. Keller, Jae Young Choi, R. Crescenzi, E. Conant, A. Maidment, D. Kontos

Proceedings Volume 8668, 866827 (2013) https://doi.org/10.1117/12.2007989

KEYWORDS: Breast, Statistical analysis, Mammography, Tissues, Physics, Breast cancer, Imaging systems, Digital mammography, Image quality, X-ray imaging

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Proceedings Article | 28 February 2013 Paper

Texture feature standardization in digital mammography for improving generalizability across devices

Yan Wang, Brad Keller, Yuanjie Zheng, Raymond Acciavatti, James Gee, Andrew D. A. Maidment, Despina Kontos

Proceedings Volume 8670, 867026 (2013) https://doi.org/10.1117/12.2008149

KEYWORDS: Breast, Imaging systems, Breast cancer, Sensors, Digital mammography, Feature extraction, Image processing, Modulation transfer functions, Control systems, Spatial resolution

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Growing evidence suggests a relationship between mammographic texture and breast cancer risk. For studies performing texture analysis on digital mammography (DM) images from various DM systems, it is important to evaluate if different systems could introduce inherent differences in the images analyzed and how to construct a methodological framework to identify and standardize such effects, if these differences exist. In this study, we compared two DM systems, the GE Senographe 2000D and DS using a validated physical breast phantom (Rachel, Gammex). The GE 2000D and DS systems use the same detector, but a different automated exposure control (AEC) system, resulting in differences in dose performance. On each system, images of the phantom are acquired five times in the Cranio-Caudal (CC) view with the same clinically optimized phototimer setting. Three classes of texture features, namely grey-level histogram, cooccurrence, and run-length texture features (a total of 26 features), are generated within the breast region from the raw DM images and compared between the two imaging systems. To alleviate system effects, a range of standardization steps are applied to the feature extraction process: z-score normalization is performed as the initial step to standardize image intensities, and the parameters in generating co-occurrence features are varied to decrease system differences introduced by detector blurring effects. To identify texture features robust to detectors (i.e. the ones minimally affected only by electronic noise), the distribution of each texture feature is compared between the two systems using the Kolmogorov-Smirnov (K-S) test at 0.05 significance, where features with p>0.05 are deemed robust to inherent system differences. Our approach could provide a basis for texture feature standardization across different DM imaging systems and provide a systematic methodology for selecting generalizable texture descriptors in breast cancer risk assessment.

Proceedings Article | 28 February 2013 Paper

A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

Yuanjie Zheng, Yan Wang, Brad Keller, Emily Conant, James Gee, Despina Kontos

Proceedings Volume 8670, 86701B (2013) https://doi.org/10.1117/12.2008155

KEYWORDS: Breast, Breast cancer, Tissues, Digital mammography, Mammography, Image classification, Cancer, Image segmentation, Software development, Machine vision

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Proceedings Article | 3 March 2012 Paper

Model-based estimation of breast percent density in raw and processed full-field digital mammography images from image-acquisition physics and patient-image characteristics

Brad Keller, Diane Nathan, Emily Conant, Despina Kontos

Proceedings Volume 8313, 83130K (2012) https://doi.org/10.1117/12.912368

KEYWORDS: Breast, Mammography, Model-based design, Image processing, Digital mammography, Physics, Tissues, Breast cancer, Image acquisition, Tumor growth modeling

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Proceedings Article | 9 March 2010 Paper

Variation compensation and analysis on diaphragm curvature analysis for emphysema quantification on whole lung CT scans

Brad Keller, Anthony Reeves, R. Graham Barr, David Yankelevitz, Claudia Henschke

Proceedings Volume 7624, 762432 (2010) https://doi.org/10.1117/12.844012

KEYWORDS: Emphysema, Lung, Computed tomography, Medical imaging, Computer aided diagnosis and therapy, Current controlled current source

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Proceedings Article | 9 March 2010 Paper

Automated quantification of pulmonary emphysema from computed tomography scans: comparison of variation and correlation of common measures in a large cohort

Brad Keller, Anthony Reeves, David Yankelevitz, Claudia Henschke

Proceedings Volume 7624, 76241Q (2010) https://doi.org/10.1117/12.844013

KEYWORDS: Emphysema, Computed tomography, Fractal analysis, Medical imaging, Computer aided diagnosis and therapy, Current controlled current source, Tomography, Lung

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Proceedings Article | 28 February 2009 Paper

Emphysema quantification from CT scans using novel application of diaphragm curvature estimation: comparison with standard quantification methods and pulmonary function data

Brad Keller, Anthony Reeves, David Yankelevitz, Claudia Henschke, R. Graham Barr

Proceedings Volume 7260, 726032 (2009) https://doi.org/10.1117/12.812247

KEYWORDS: Emphysema, Lung, Computed tomography, Scanners, Patents, Chronic obstructive pulmonary disease, Signal attenuation, Medical research, Computer aided diagnosis and therapy, Sensors

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Proceedings Article | 28 February 2009 Paper

Quantitative assessment of emphysema from whole lung CT scans: comparison with visual grading

Brad Keller, Anthony Reeves, Tatiyana Apanosovich, Jianwei Wang, David Yankelevitz, Claudia Henschke

Proceedings Volume 7260, 726008 (2009) https://doi.org/10.1117/12.812254

KEYWORDS: Lung, Emphysema, Visualization, Computed tomography, Fractal analysis, Chronic obstructive pulmonary disease, Visual analytics, Patents, Computing systems, Medical research

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Proceedings Article | 1 April 2008 Paper

Variation of quantitative emphysema measurements from CT scans

Brad Keller, Anthony Reeves, Claudia Henschke, R. Graham Barr, David Yankelevitz

Proceedings Volume 6915, 69152I (2008) https://doi.org/10.1117/12.770844

KEYWORDS: Emphysema, Fractal analysis, Lung, Computed tomography, Scanners, Data acquisition, Visualization, Computer aided diagnosis and therapy, Computing systems, Lung cancer

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Proceedings Article | 30 March 2007 Paper

Semi-automated location identification of catheters in digital chest radiographs

Brad Keller, Anthony Reeves, Matthew Cham, Claudia Henschke, David Yankelevitz

Proceedings Volume 6514, 65141O (2007) https://doi.org/10.1117/12.707769

KEYWORDS: Chest imaging, Detection and tracking algorithms, Radiography, Signal attenuation, Electronic filtering, Image filtering, Chest, Algorithm development, Signal detection, Image quality standards

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Showing 5 of 20 publications

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