Prof. David Gur Profile

Prof. David Gur

at Univ of Pittsburgh School of Medicine

SPIE Involvement:

Author

Publications (88)

Proceedings Article | 7 March 2018 Paper

Can a totally different approach to soft tissue computer aided detection (CADe) result in affecting radiologists' decisions?

David Gur

Proceedings Volume 10577, 105771B (2018) https://doi.org/10.1117/12.2301251

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Mammography, Tissues, Breast, Computer aided design, Radiology, Breast cancer

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Proceedings Article | 7 March 2018 Paper

Can a limited double reading/second opinion of initially recalled breast ultrasound screening examinations improve radiologists' performances?

David Gur, Kimberly Harnist, Terri-Ann Gizienski, Margarita Zuley, Christiane Hakim, Amy Lu, Jules Sumkin, Gordon Abrams, Marie Ganott, Amy Kelly, Cathy Tyma, Thomas Chang, Marcella Bӧhm-Vélez, Robin Sobolewski

Proceedings Volume 10577, 1057710 (2018) https://doi.org/10.1117/12.2301249

KEYWORDS: Cancer, Ultrasonography, Breast, Digital breast tomosynthesis, Mammography, Diagnostics, Radiology, Tissues, Statistical analysis, Breast cancer

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Proceedings Article | 10 March 2017 Presentation + Paper

The effect of prevalence of disease on performance of residents and fellows during training for interpreting DBT in a test-train-test observer study

Christiane Hakim, Lauren Chang Sen, Andrew Degnan, Joseph Delic, Sarah Pai, Hersh Sagreiya, Mark Sparrow, Ernestine Thomas, Michael Yannes, David Gur

Proceedings Volume 10136, 1013603 (2017) https://doi.org/10.1117/12.2249341

KEYWORDS: Digital breast tomosynthesis, Breast imaging, Electronic filtering, Cancer, Digital imaging, Breast, Diagnostics, Image compression, Radiology, Biopsy

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Proceedings Article | 10 March 2017 Paper

The development and testing of a unique and flexible training module for residents and fellows using digital breast tomosythesis (DBT)

Christiane Hakim, John Drescher, Jill King, Durwin Logue, Amy Klym, David Gur

Proceedings Volume 10136, 101361L (2017) https://doi.org/10.1117/12.2248911

KEYWORDS: Bismuth, Digital breast tomosynthesis, Breast, Breast imaging, Radiology, Diagnostics, Pathology, Biopsy, Cancer, Distortion

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Proceedings Article | 24 March 2016 Paper

Changes in frequency of recall recommendations of examinations depicting cancer with the availability of either priors or digital breast tomosynthesis

Christiane Hakim, Andriy Bandos, Marie Ganott, Victor Catullo, Denise Chough, Amy Kelly, Dilip Shinde, Jules Sumkin, Luisa Wallace, Robert Nishikawa, David Gur

Proceedings Volume 9787, 97871A (2016) https://doi.org/10.1117/12.2213075

KEYWORDS: Digital breast tomosynthesis, Cancer, Breast, Breast cancer, Binary data, Environmental sensing, Breast imaging, Image analysis, Radiology, Digital mammography, Medical imaging

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Proceedings Article | 20 March 2015 Paper

Signal enhancement ratio (SER) quantified from breast DCE-MRI and breast cancer risk

Shandong Wu, Brenda Kurland, Wendie Berg, Margarita Zuley, Rachel Jankowitz, Jules Sumkin, David Gur

Proceedings Volume 9414, 94140M (2015) https://doi.org/10.1117/12.2082466

KEYWORDS: Breast, Breast cancer, Magnetic resonance imaging, Tissues, Control systems, Cancer, Tumors, Mammography, Statistical analysis, Biopsy

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Breast magnetic resonance imaging (MRI) is recommended as an adjunct to mammography for women who are considered at elevated risk of developing breast cancer. As a key component of breast MRI, dynamic contrast-enhanced MRI (DCE-MRI) uses a contrast agent to provide high intensity contrast between breast tissues, making it sensitive to tissue composition and vascularity. Breast DCE-MRI characterizes certain physiologic properties of breast tissue that are potentially related to breast cancer risk. Studies have shown that increased background parenchymal enhancement (BPE), which is the contrast enhancement occurring in normal cancer-unaffected breast tissues in post-contrast sequences, predicts increased breast cancer risk. Signal enhancement ratio (SER) computed from pre-contrast and post-contrast sequences in DCE-MRI measures change in signal intensity due to contrast uptake over time and is a measure of contrast enhancement kinetics. SER quantified in breast tumor has been shown potential as a biomarker for characterizing tumor response to treatments. In this work we investigated the relationship between quantitative measures of SER and breast cancer risk. A pilot retrospective case-control study was performed using a cohort of 102 women, consisting of 51 women who had diagnosed with unilateral breast cancer and 51 matched controls (by age and MRI date) with a unilateral biopsy-proven benign lesion. SER was quantified using fully-automated computerized algorithms and three SER-derived quantitative volume measures were compared between the cancer cases and controls using logistic regression analysis. Our preliminary results showed that SER is associated with breast cancer risk, after adjustment for the Breast Imaging Reporting and Data System (BI-RADS)-based mammographic breast density measures. This pilot study indicated that SER has potential for use as a risk factor for breast cancer risk assessment in women at elevated risk of developing breast cancer.

Proceedings Article | 21 March 2014 Paper

Fusion of digital breast tomosynthesis images via wavelet synthesis for improved lesion conspicuity

Harishwaran Hariharan, Victor Pomponiu, Bin Zheng, Bruce Whiting, David Gur

Proceedings Volume 9034, 90342B (2014) https://doi.org/10.1117/12.2043799

KEYWORDS: Image fusion, Digital breast tomosynthesis, Wavelets, Tissues, 3D image processing, Breast, Discrete wavelet transforms, Breast cancer, Visualization, Digital mammography

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Proceedings Article | 18 March 2014 Paper

Improving breast mass detection using histogram of oriented gradients

Victor Pomponiu, Harishwaran Hariharan, Bin Zheng, David Gur

Proceedings Volume 9035, 90351R (2014) https://doi.org/10.1117/12.2044281

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

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Proceedings Article | 29 March 2013 Paper

Texture-based CT Image analysis of asthma

Harishwaran Hariharan, Sally Wenzel, Bin Zheng, Bruce Whiting, Jiantao Pu, David Gur, J. Ken Leader

Proceedings Volume 8672, 86720D (2013) https://doi.org/10.1117/12.2006887

KEYWORDS: Computed tomography, Lung, Image analysis, Image segmentation, Statistical analysis, Medical imaging, Visualization, Pulmonary function tests, Analytical research, Image processing

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Proceedings Article | 28 March 2013 Paper

Prediction of near-term breast cancer risk using a Bayesian belief network

Bin Zheng, Pandiyarajan Ramalingam, Harishwaran Hariharan, Joseph Leader, David Gur

Proceedings Volume 8673, 86731F (2013) https://doi.org/10.1117/12.2006383

KEYWORDS: Breast cancer, Breast, Cancer, Mammography, Tumor growth modeling, Tissues, Image processing, Image segmentation, Medical imaging, Databases

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Proceedings Article | 6 March 2013 Paper

Resonance-frequency based electrical impedance spectroscopy and its detection sensitivity to breast lesions

Sreeram Dhurjaty, Bin Zheng, David Gur

Proceedings Volume 8668, 86684U (2013) https://doi.org/10.1117/12.2006380

KEYWORDS: Breast, Capacitance, Tissues, Breast cancer, Resistance, Electrodes, Signal detection, Data modeling, Cancer, Dielectric spectroscopy

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Proceedings Article | 16 April 2012 Paper

Classification of CT examinations for COPD visual severity analysis

Jun Tan, Bin Zheng, Xingwei Wang, Jiantao Pu, David Gur, Frank Sciurba, J. Ken Leader

Proceedings Volume 8317, 831723 (2012) https://doi.org/10.1117/12.911751

KEYWORDS: Visualization, Emphysema, Image segmentation, Lung, Computed tomography, Chronic obstructive pulmonary disease, Visual analytics, Feature extraction, Wavelets, Image classification

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Proceedings Article | 22 February 2012 Paper

Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations

David Gur, Margarita Zuley, Jules Sumkin, Christiane Hakim, Denise Chough, Linda Lovy, Cynthia Sobran, Durwin Logue, Bin Zheng, Amy Klym

Proceedings Volume 8318, 83181D (2012) https://doi.org/10.1117/12.910596

KEYWORDS: Digital breast tomosynthesis, Mammography, Cancer, Breast, Breast cancer, Radiology, Databases, Architectural distortion, Pathology, Breast imaging

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Proceedings Article | 22 February 2012 Paper

Assessment of two mammographic density related features in predicting near-term breast cancer risk

Bin Zheng, Jules Sumkin, Margarita Zuley, Xingwei Wang, Amy Klym, David Gur

Proceedings Volume 8318, 83181E (2012) https://doi.org/10.1117/12.910630

KEYWORDS: Breast cancer, Breast, Cancer, Mammography, Tumor growth modeling, Databases, Tissues, Image segmentation, Detector development, Computer aided diagnosis and therapy

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Proceedings Article | 22 February 2012 Paper

Classification of thyroid nodules using a resonance-frequency-based electrical impedance spectroscopy: progress assessment

Bin Zheng, Mitchell Tublin, Dror Lederman, Amy Klym, Erica Brown, David Gur

Proceedings Volume 8318, 83181G (2012) https://doi.org/10.1117/12.911553

KEYWORDS: Cancer, Diagnostics, Neck, Signal detection, Computing systems, Dielectric spectroscopy, Surgery, Sensors, Ultrasonography, Measurement devices

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Proceedings Article | 16 March 2011 Paper

Quantitative computed tomography of lung parenchyma in patients with emphysema: analysis of higher-density lung regions

Dror Lederman, Joseph Leader, Bin Zheng, Frank Sciurba, Jun Tan, David Gur

Proceedings Volume 7965, 79651X (2011) https://doi.org/10.1117/12.877542

KEYWORDS: Lung, Emphysema, Computed tomography, Photovoltaics, Image segmentation, Visualization, X-ray computed tomography, Solar cells, Chronic obstructive pulmonary disease, Statistical analysis

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Proceedings Article | 9 March 2011 Paper

Texture-based segmentation and analysis of emphysema depicted on CT images

Jun Tan, Bin Zheng, Xingwei Wang, Dror Lederman, Jiantao Pu, Frank Sciurba, David Gur, J. Ken Leader

Proceedings Volume 7965, 79651V (2011) https://doi.org/10.1117/12.878279

KEYWORDS: Emphysema, Image segmentation, Lung, Computed tomography, Fractal analysis, Optical inspection, Chronic obstructive pulmonary disease, Visualization, Feature extraction, Lung cancer

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Proceedings Article | 9 March 2011 Paper

Multi-probe-based resonance-frequency electrical impedance spectroscopy for detection of suspicious breast lesions: improving performance using partial ROC optimization

Dror Lederman, Bin Zheng, Xingwei Wang, Xiao Hui Wang, David Gur

Proceedings Volume 7963, 79631Z (2011) https://doi.org/10.1117/12.877380

KEYWORDS: Breast, Breast cancer, Mammography, Tissues, Biopsy, Sensors, Dielectric spectroscopy, Computer aided diagnosis and therapy, Machine learning, Signal detection

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Proceedings Article | 3 March 2011 Paper

Assessing risk of thyroid cancer using resonance-frequency based electrical impedance measurements

Bin Zheng, Mitchell Tublin, Dror Lederman, Amy Klym, Erica Brown, David Gur

Proceedings Volume 7966, 79661A (2011) https://doi.org/10.1117/12.877163

KEYWORDS: Diagnostics, Cancer, Neck, Surgery, Tissues, Dielectric spectroscopy, Skin, Breast, Biopsy, Breast cancer

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Proceedings Article | 3 March 2011 Paper

Fusion of classifiers for REIS-based detection of suspicious breast lesions

Dror Lederman, Xingwei Wang, Bin Zheng, Jules Sumkin, Mitchell Tublin, David Gur

Proceedings Volume 7966, 79661C (2011) https://doi.org/10.1117/12.877368

KEYWORDS: Breast, Breast cancer, Biopsy, Mammography, Diagnostics, Sensors, Feature extraction, Skin, Machine learning, Cancer

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Proceedings Article | 24 February 2010 Paper

A support vector machine designed to identify breasts at high risk using multi-probe generated REIS signals: a preliminary assessment

David Gur, Bin Zheng, Dror Lederman, Sreeram Dhurjaty, Jules Sumkin, Margarita Zuley

Proceedings Volume 7627, 76271B (2010) https://doi.org/10.1117/12.844452

KEYWORDS: Breast, Biopsy, Signal detection, Sensors, Tissues, Signal generators, Imaging systems, Nipple, Breast cancer, Skin

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Proceedings Article | 27 March 2009 Paper

Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest

Sang Cheol Park, Won Pil Kim, Bin Zheng, Joseph Leader, Jiantao Pu, Jun Tan, David Gur

Proceedings Volume 7259, 72593U (2009) https://doi.org/10.1117/12.810947

KEYWORDS: Image segmentation, Lung, Computed tomography, 3D image processing, X-ray computed tomography, Chronic obstructive pulmonary disease, Volume rendering, Bone, Bronchoscopy, Chest

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Proceedings Article | 13 March 2009 Paper

Developing and testing a multi-probe resonance electrical impedance spectroscopy system for detecting breast abnormalities

David Gur, Bin Zheng, Sreeram Dhurjaty, Gene Wolfe, Mary Fradin, Richard Weil, Jules Sumkin, Margarita Zuley

Proceedings Volume 7263, 72631F (2009) https://doi.org/10.1117/12.811017

KEYWORDS: Breast, Sensors, Signal detection, Breast cancer, Tissues, Dielectric spectroscopy, Mammography, Biopsy, Cancer, Nipple

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In our previous study, we reported on the development and preliminary testing of a prototype resonance electrical impedance spectroscopy (REIS) system with a pair of probes. Although our pilot study on 150 young women ranging from 30 to 50 years old indicated the feasibility of using REIS output sweep signals to classify between the women who had negative examinations and those who would ultimately be recommended for biopsy, the detection sensitivity was relatively low. To improve performance when using REIS technology, we recently developed a new multi-probe based REIS system. The system consists of a sensor module box that can be easily lifted along a vertical support device to fit women of different height. Two user selectable breast placement "cups" with different curvatures are included in the system. Seven probes are mounted on each of the cups on opposing sides of the sensor box. By rotating the sensor box, the technologist can select the detection sensor cup that better fits the breast size of the woman being examined. One probe is mounted in the cup center for direct contact with the nipple and the other six probes are uniformly distributed along an outside circle to enable contact with six points on the outer and inner breast skin surfaces. The outer probes are located at a distance of 60mm away from the center (nipple) probe. The system automatically monitors the quality of the contact between the breast surface and each of the seven probes and data acquisition can only be initiated when adequate contact is confirmed. The measurement time for each breast is approximately 15 seconds during which time the system records 121 REIS signal sweep outputs generated from 200 KHz to 800 KHz at 5 KHz increments for all preselected probe pairs. Currently we are measuring 6 pairs between the center probe and each of six probes located on the outer circle as well as two pairs between probe pairs on the outer circle. This new REIS system has been installed in our clinical breast imaging facility. We are conducting a prospective study to assess performance when using this REIS system under an approved IRB protocol. Over 200 examinations have been conducted to date. Our experience showed that this new REIS system was easy to operate and the REIS examination was fast and considered "comfortable" by examinees since the women presses her breast into the cup herself without any need for forced breast compression, and all but a few highly sensitive women have any sensation of an electrical current during the measurement.

Proceedings Article | 3 March 2009 Paper

Computer-aided detection of HER2 amplification status using FISH images: a preliminary study

Bin Zheng, Xiao-Hui Wang, Urvashi Surti, Rohit Bhargava, David Gur

Proceedings Volume 7260, 726028 (2009) https://doi.org/10.1117/12.810155

KEYWORDS: Image segmentation, Signal detection, Computer aided design, Breast cancer, Image analysis, Statistical analysis, Luminescence, Computer aided diagnosis and therapy, Genetics

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The amplification status of human epidermal growth factor receptors 2 (HER2) genes is strongly associated with clinical outcome in patients with breast cancer. The American Society of Clinical Oncology Tumor Marker Guidelines Panel has recommended routine testing of HER2 status on all newly diagnosed metastatic breast cancers since 2001. Although fluorescent in situ hybridization (FISH) technology provides superior accuracy as compared with other approaches, current manual FISH analysis methods are somewhat subjective, tedious, and may introduce interreader variability. The goal of this preliminary study is to develop and test a computer-aided detection (CAD) scheme to assess HER2 status using FISH images. Forty FISH images were selected for this study from our genetic laboratory. The CAD scheme first applies an adaptive, iterative threshold method followed by a labeling algorithm to segment cells of possible interest. A set of classification rules is then used to identify analyzable interphase cells and discard nonanalyzable cells due to cell overlapping and/or other image staining debris (or artifacts). The scheme then maps the detected analyzable cells onto two other gray scale images corresponding to the red and green color of the original image followed by application of a raster scan and labeling algorithms to separately detect the HER-2/neu ("red") and CEP17 ("green") FISH signals. A simple distance based criterion is applied to detect and merge split FISH signals within each cell. The CAD scheme computes the ratio between independent "red" and "green" FISH signals of all analyzable cells identified on an image. If the ratio is ≥ 2.0, the FISH image is assumed to have been acquired from a HER2+ case; otherwise, the FISH image is assumed to have been acquired from HER2- case. When we applied the CAD scheme to the testing dataset, the average computed HER2 amplification ratios were 1.06±0.25 and 2.53±0.81 for HER2- and HER2+ samples, respectively. The results show that the CAD scheme has the ability to automatically detect HER2 status using FISH images. The success of CAD-guided FISH image analysis could result in a more objective, consistent, and efficient approach in determining HER2 status of breast cancers.

Proceedings Article | 28 February 2009 Paper

Association between lung function and airway wall density

J. Ken Leader, Bin Zheng, Carl Fuhrman, John Tedrow, Sang C. Park, Jun Tan, Jiantao Pu, John Drescher, David Gur, Frank Sciurba

Proceedings Volume 7262, 72622J (2009) https://doi.org/10.1117/12.811768

KEYWORDS: Lung, Chronic obstructive pulmonary disease, Computed tomography, Image segmentation, Gold, Signal attenuation, Tissues, Medium wave, Medicine, In vivo imaging

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Proceedings Article | 17 March 2008 Paper

Applying a 2D based CAD scheme for detecting micro-calcification clusters using digital breast tomosynthesis images: an assessment

Sang Cheol Park, Bin Zheng, Xiao-Hui Wang, David Gur

Proceedings Volume 6915, 691507 (2008) https://doi.org/10.1117/12.768534

KEYWORDS: Digital breast tomosynthesis, Computer aided design, Mammography, Computer aided diagnosis and therapy, Breast, Visualization, 3D image processing, Signal to noise ratio, Image filtering

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Digital breast tomosynthesis (DBT) has emerged as a promising imaging modality for screening mammography. However, visually detecting micro-calcification clusters depicted on DBT images is a difficult task. Computer-aided detection (CAD) schemes for detecting micro-calcification clusters depicted on mammograms can achieve high performance and the use of CAD results can assist radiologists in detecting subtle micro-calcification clusters. In this study, we compared the performance of an available 2D based CAD scheme with one that includes a new grouping and scoring method when applied to both projection and reconstructed DBT images. We selected a dataset involving 96 DBT examinations acquired on 45 women. Each DBT image set included 11 low dose projection images and a varying number of reconstructed image slices ranging from 18 to 87. In this dataset 20 true-positive micro-calcification clusters were visually detected on the projection images and 40 were visually detected on the reconstructed images, respectively. We first applied the CAD scheme that was previously developed in our laboratory to the DBT dataset. We then tested a new grouping method that defines an independent cluster by grouping the same cluster detected on different projection or reconstructed images. We then compared four scoring methods to assess the CAD performance. The maximum sensitivity level observed for the different grouping and scoring methods were 70% and 88% for the projection and reconstructed images with a maximum false-positive rate of 4.0 and 15.9 per examination, respectively. This preliminary study demonstrates that (1) among the maximum, the minimum or the average CAD generated scores, using the maximum score of the grouped cluster regions achieved the highest performance level, (2) the histogram based scoring method is reasonably effective in reducing false-positive detections on the projection images but the overall CAD sensitivity is lower due to lower signal-to-noise ratio, and (3) CAD achieved higher sensitivity and higher false-positive rate (per examination) on the reconstructed images. We concluded that without changing the detection threshold or performing pre-filtering to possibly increase detection sensitivity, current CAD schemes developed and optimized for 2D mammograms perform relatively poorly and need to be re-optimized using DBT datasets and new grouping and scoring methods need to be incorporated into the schemes if these are to be used on the DBT examinations.

Proceedings Article | 17 March 2008 Paper

Assessment of the relationship between lesion segmentation accuracy and computer-aided diagnosis scheme performance

Bin Zheng, Jiantao Pu, Sang Cheol Park, Margarita Zuley, David Gur

Proceedings Volume 6915, 691530 (2008) https://doi.org/10.1117/12.768705

KEYWORDS: Image segmentation, Computer aided diagnosis and therapy, Computer aided design, Breast, Tissues, Image processing algorithms and systems, Mammography, Algorithm development, Databases, Detection and tracking algorithms

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Proceedings Article | 12 March 2008 Paper

The relation of airway size to lung function

J. Ken Leader, Bin Zheng, Frank Sciurba, Carl Fuhrman, Jessica Bon, Sang Park, Jiantao Pu, David Gur

Proceedings Volume 6916, 691623 (2008) https://doi.org/10.1117/12.770886

KEYWORDS: Lung, Computed tomography, Chronic obstructive pulmonary disease, Gold, Image segmentation, Spatial resolution, Adaptive optics, Spirometry, Visualization, Tissues

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Proceedings Article | 6 March 2008 Paper

Assembling a prototype resonance electrical impedance spectroscopy system for breast tissue signal detection: preliminary assessment

Jules Sumkin, Bin Zheng, Michelle Gruss, John Drescher, Joseph Leader, Walter Good, Amy Lu, Cathy Cohen, Ratan Shah, Margarita Zuley, David Gur

Proceedings Volume 6917, 691716 (2008) https://doi.org/10.1117/12.770457

KEYWORDS: Breast, Biopsy, Tissues, Breast cancer, Prototyping, Dielectric spectroscopy, Mammography, Signal detection, Data acquisition, Tissue optics

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Using electrical impedance spectroscopy (EIS) technology to detect breast abnormalities in general and cancer in particular has been attracting research interests for decades. Large clinical tests suggest that current EIS systems can achieve high specificity (≥ 90%) at a relatively low sensitivity ranging from 15% to 35%. In this study, we explore a new resonance frequency based electrical impedance spectroscopy (REIS) technology to measure breast tissue EIS signals in vivo, which aims to be more sensitive to small tissue changes. Through collaboration between our imaging research group and a commercial company, a unique prototype REIS system has been assembled and preliminary signal acquisition has commenced. This REIS system has two detection probes mounted in the two ends of a Y-shape support device with probe separation of 60 mm. During REIS measurement, one probe touches the nipple and the other touches to an outer point of the breast. The electronic system continuously generates sweeps of multi-frequency electrical pulses ranging from 100 to 4100 kHz. The maximum electric voltage and the current applied to the probes are 1.5V and 30mA, respectively. Once a "record" command is entered, multi-frequency sweeps are recorded every 12 seconds until the program receives a "stop recording" command. In our imaging center, we have collected REIS measurements from 150 women under an IRB approved protocol. The database includes 58 biopsy cases, 78 screening negative cases, and other "recalled" cases (for additional imaging procedures). We measured eight signal features from the effective REIS sweep of each breast. We applied a multi-feature based artificial neural network (ANN) to classify between "biopsy" and normal "non-biopsy" breasts. The ANN performance is evaluated using a leave-one-out validation method and ROC analysis. We conducted two experiments. The first experiment attempted to classify 58 "biopsy" breasts and 58 "non-biopsy" breasts acquired on 58 women each having one breast recommended for biopsy. The second experiment attempted to classify 58 "biopsy" breasts and 58 negative breasts from the set of screening negative cases. The areas under ROC curves are 0.679 ± 0.033 and 0.606 ± 0.035 for the first and the second experiment, respectively. The preliminary results demonstrate (1) even with this rudimentary system with only one paired probes there is a measurable signal of changes in breast tissue demonstrating the feasibility of applying REIS technology for identifying at least some women with highly suspicious breast abnormalities and (2) the electromagnetic asymmetry between two breasts may be more sensitive in detecting changes in the abnormal breast. To further improve the REIS system performance, we are currently designing a new REIS system with multiple electrical probes and a more sophisticated analysis scheme.

Proceedings Article | 30 March 2007 Paper

Mass margins spiculations: agreement between ratings by observers and a computer scheme

Bin Zheng, Gordon Abrams, Lara Hardesty, Sang Cheol Park, John Drescher, Glenn Maitz, David Gur

Proceedings Volume 6514, 65141P (2007) https://doi.org/10.1117/12.705754

KEYWORDS: Image segmentation, Visualization, Computer aided diagnosis and therapy, Computer aided design, Image processing algorithms and systems, Mammography, Tissues, Breast, Binary data, Detection and tracking algorithms

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This study investigated the agreement between breast mass spiculation levels as rated subjectively by observers and a computer scheme. An image dataset with 1,263 mass regions was selected. First, three experienced observers independently rated subjectively the visualized spiculation levels of these mass regions and classified them into three categories (none/minimal, moderate, and severe/significant). We then developed a computerized scheme to detect mass margins and classify the spiculation levels of the suspected mass regions. The scheme applied a hybrid region growth algorithm to segment the mass regions. An edge map was computed inside a 30-pixel-wide band surrounding the mass boundary contour. The scheme then applied a threshold to convert the edge map into a binary image followed by labeling and detecting line orientation. In the original edge map the scheme computed the average local pixel value fluctuation. In the binary edge map, the scheme computed the ratio between the number of "spiculated" pixels and the number of total pixels inside the band. The scheme also computed mass region conspicuity using the original image. Using these three features, a Bayesian Belief Network (BBN) was built to classify mass regions into one of the three spiculation categories. We compared the inter-observer variation as well as agreement levels between the subjective and computerized ratings. Agreement rates between paired observers ranged from 41.3% to 58.8% (Kappa = 0.136 to 0.309). The agreement between the computer scheme and observers' average rating was 49.2% (Kappa = 0.218). This study demonstrated a large inter-observer variability in subjective rating of mass speculation levels as well as a large difference between the rating results of a computerized scheme and observers. As a result, in an Interactive Computer-Aided Diagnosis (ICAD) environment, CAD-selected reference regions may be considered "very similar" by some observers and "not similar" by others. Hence, improving the selection of actually visually similar reference regions by a computerized scheme remains an important yet unsolved task for ICAD development.

Proceedings Article | 29 March 2007 Paper

Quantification of airway morphometry: the effect of CT acquisition and reconstruction parameters

J. Ken Leader, Bin Zheng, Frank Sciurba, Harvey Coxson, Carl Fuhrman, Jessica McMurray, Sang Park, Glenn Maitz, David Gur

Proceedings Volume 6511, 65111R (2007) https://doi.org/10.1117/12.709940

KEYWORDS: Lung, Computed tomography, Image segmentation, Manufacturing, CT reconstruction, Tissues, Chronic obstructive pulmonary disease, Medicine, Scanners, X-ray computed tomography

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Proceedings Article | 8 March 2007 Paper

Evaluation of an interactive computer-aided diagnosis (ICAD) system for mammography: a pilot study

Bin Zheng, Gordon Abrams, Cynthia Britton, Christiane Hakim, Amy Lu, Ronald Clearfield, John Drescher, Glenn Maitz, David Gur

Proceedings Volume 6515, 65151M (2007) https://doi.org/10.1117/12.705756

KEYWORDS: Visualization, Computer aided diagnosis and therapy, Mammography, Computer aided design, Image segmentation, Library classification systems, Breast, Computing systems, LCDs, Cancer

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Five radiologists detected suspicious mass regions depicted on mammograms acquired from 32 examinations during this pilot study. Among these, 24 examinations depicted subtle masses (12 malignant and 12 benign) and 8 were negative. Each observer interpreted a case in a sequential order under three reading modes. In mode one, the observer interpreted images without viewing CAD-generated cues. The observer provided two likelihood scores (for detection and classification) for each identified suspicious region. In mode two, CAD-cued results were provided and the observer could decide whether to make any changes in the previous ratings. In mode three, each observer was forced to query at least one suspected region. Once a region was queried, CAD scheme automatically segmented the mass region and computed a set of image features. Using a conditioned k-nearest neighbor (KNN) algorithm, six reference regions that were considered "the most similar" to the queried region were selected and displayed along with CAD-generated scores. Again, the observer had an option to change previous ratings. Experimental results were analyzed using ROC method. Five observers marked total 271, 276, and 281 mass regions under the three reading modes, respectively. In mode 2 observers marked 5 new suspected mass regions and did not make any changes in previously rated detection or classification scores. In mode three, although observers queried 18 additional regions, 13 were discarded and 5 were marked with region specific related scores. The observers also changed previous rating scores of 28 mass regions marked during mode one. The areas under ROC curves for individual readers ranged from 0.51 to 0.71 for mass detection (p = 0.67) and from 0.50 to 0.73 for mass classification (p = 0.43). This pilot study suggested that using ICAD could increase radiologists' confidence in their decision making. We also found that because radiologists tend to accept a higher false-positive rate in a laboratory environment, once they made their detection decision during the initial reading, they are frequently reluctant to make changes during the following modes. Hence, while simple and efficient operationally, the sequential reading mode may not be an optimal approach to evaluate the actual utility of ICAD.

Proceedings Article | 7 March 2007 Paper

Application of 3D geometric tensors for segmenting cylindrical tree structures from volumetric datasets

Walter Good, Xiao Hui Wang, Carl Fuhrman, Jules Sumkin, Glenn Maitz, Joseph Leader, Cynthia Britton, David Gur

Proceedings Volume 6512, 65123R (2007) https://doi.org/10.1117/12.709823

KEYWORDS: Image segmentation, 3D applications, Matrices, Gold, Diagnostics, Detection and tracking algorithms, Image filtering, Statistical analysis, Chronic obstructive pulmonary disease, Radiology

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Proceedings Article | 17 March 2006 Paper

Actual versus intended use of CAD systems in the clinical environment

Bin Zheng, Denise Chough, Perrin Ronald, Cathy Cohen, Christiane Hakim, Gordon Abrams, Marie Ganott, Luisa Wallace, Rattan Shah, Jules Sumkin, David Gur

Proceedings Volume 6146, 614602 (2006) https://doi.org/10.1117/12.649595

KEYWORDS: Computer aided diagnosis and therapy, Computer aided design, CAD systems, Glasses, Mammography, Cancer, Breast, Computing systems, Radiology

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Proceedings Article | 13 March 2006 Paper

CT acquisition technique and quantitative analysis of the lung parenchyma: variability and corrections

Bin Zheng, J. Leader, Harvey Coxson, Frank Scuirba, Carl Fuhrman, Arzu Balkan, Joel Weissfeld, Glenn Maitz, David Gur

Proceedings Volume 6143, 61432S (2006) https://doi.org/10.1117/12.654532

KEYWORDS: Emphysema, Lung, Computed tomography, Chronic obstructive pulmonary disease, X-ray computed tomography, Gold, CT reconstruction, Diagnostics, Visualization, Medicine

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Proceedings Article | 13 March 2006 Paper

Airway morphometry in the lungs as depicted in chest CT examinations variability of measurements

J. Leader, Bin Zheng, Frank Scuirba, Harvey Coxson, Joel Weissfeld, Carl Fuhrman, Glenn Maitz, David Gur

Proceedings Volume 6143, 61432R (2006) https://doi.org/10.1117/12.653984

KEYWORDS: Image segmentation, Lung, Computed tomography, Chronic obstructive pulmonary disease, Emphysema, Gold, Visualization, Tissues, Adaptive optics, Chest

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Proceedings Article | 10 March 2006 Paper

Increasing sensitivity of masses cued on both views by CAD

Bin Zheng, Glen Maitz, Joseph Leader, David Gur

Proceedings Volume 6144, 61441Y (2006) https://doi.org/10.1117/12.649629

KEYWORDS: Computer aided design, Computer aided diagnosis and therapy, Image processing, Cancer, Nipple, Environmental sensing, CAD systems, Chest, Databases

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Although CAD schemes can detect a high percentage of subtle cancers depicted on "prior" and false-negative cases, radiologists frequently discard most of CAD-cued subtle masses in the clinical environment. As a result of the relatively high false-positive detection rate and the fact that a large number of subtle masses are typically cued only on one view cause radiologists to rely less on (and often ignore) CAD-cued masses. In this study, we present a multi-view based method to increase the number of actual masses that are cued by the CAD scheme on both (ipso-lateral) views and at the same time limiting the overall "case-based" false-positive detection rate. The new scheme includes a traditional single-image based CAD scheme and a multi-view processing module. An image database from 435 examinations (or a total of 1,740 images) consisting 235 examinations depicting a verified malignant mass each and 200 negative examinations was used in this study. The single-image based CAD scheme with a fixed operating threshold (i.e. 0.55) was applied to all images. For each CAD identified region (i.e. with detection score ≥ 0.55), the multi-view processing module defined a matched strip on the corresponding ipso-lateral image and identified all "matched" regions located inside the strip (including those with detection score < 0.55). All matched regions are cued on both views and unmatched regions were discarded. CAD scheme initially detected 172 true masses and 576 false-positive regions. Of the 172 masses, 90 were detected on two views (52%) and 82 were detected only on one view. Of the 576 false-positive detections, only 72 pairs (14%) were considered "matched" and 432 were not, resulting in 504 case-based ("independent") cues. Case-based sensitivity and false-positive rate of the CAD scheme were 73.2% and 1.16 per case. When only matched region pairs were cued, 160 masses (68.1%) and 308 false-positive detections (0.71 per case) were identified on two views. Reducing the original operating threshold from 0.55 to 0.49 resulted in 172 masses and 413 false-positive detections (or 0.95 per case) being cued on two views. By increasing sensitivity through the reduction of the detection threshold only inside the matched strip regions in ipso-lateral images the scheme successfully cued masses on two views by maintaining detection sensitivity while reducing the "case-based" false-positive detection rate.

Proceedings Article | 6 April 2005 Paper

Teleradiology and screening mammography: a telemammography system evaluation and comparison to clinical results

Joseph Leader, Denise Chough, Ronald Clearfield, Marie Ganott, Christiane Hakim, Lara Hardesty, Betty Shindel, Jules Sumkin, John Drescher, Glenn Maitz, David Gur

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.592823

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Visualization, Image compression, Breast, Breast cancer, Imaging systems, Teleradiology, Image transmission, Image processing

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Proceedings Article | 21 May 2004 Paper

Stereo display for chest CT

Xiao Wang, Walter Good, Carl Fuhrman, Jules Sumkin, Cynthia Britton, Thomas Warfel, David Gur

Proceedings Volume 5291, (2004) https://doi.org/10.1117/12.527234

KEYWORDS: Computed tomography, Chest, Stereoscopic displays, Lung, Tissues, Transparency, 3D displays, 3D image processing, 3D modeling, Image segmentation

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Proceedings Article | 12 May 2004 Paper

Automated detection and classification of interstitial lung diseases from low-dose CT images

Bin Zheng, Joseph Leader, Carl Fuhrman, Frank Sciurba, David Gur

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.533774

KEYWORDS: Lung, Computer aided diagnosis and therapy, Computed tomography, Image segmentation, Image classification, Fractal analysis, Computer aided design, Stochastic processes, Chest, Lung cancer

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Proceedings Article | 5 May 2004 Paper

Projection models for stereo display of chest CT

Xiao Hui Wang, Walter Good, Carl Fuhrman, Jules Sumkin, Cynthia Britton, Thomas Warfel, David Gur

Proceedings Volume 5367, (2004) https://doi.org/10.1117/12.536111

KEYWORDS: Chest, 3D displays, Computed tomography, Lung, Volume rendering, Tissues, 3D image processing, Visualization, Lung cancer, 3D modeling

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Proceedings Article | 4 May 2004 Paper

Subjective assessment of high-level image compression of digitized mammograms

J. Ken Leader, Jules Sumkin, Marie Ganott, Christiane Hakim, Lara Hardesty, Ratan Shah, Luisa Wallace, Amy Klym, John Drescher, Glenn Maitz, David Gur

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.533201

KEYWORDS: Image compression, Mammography, Wavelets, Data compression, Medical imaging, Image transmission, Image quality, Breast cancer, Teleradiology, Image visualization

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Proceedings Article | 4 May 2004 Paper

Classification of breast abnormalities under different detection cueing environments

Bin Zheng, Richard Swensson, Amy Klym, Lara Hardesty, Ratan Shah, Luisa Wallace, Christiane Hakim, David Gur

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.533805

KEYWORDS: Computer aided diagnosis and therapy, Breast, Mammography, CAD systems, Environmental sensing, Computer aided design, Databases, Image classification, Diagnostics

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Proceedings Article | 22 May 2003 Paper

Impedance measurements for early detection of breast cancer in younger women: a preliminary assessment

Jules Sumkin, Alexander Stojadinovic, Michelle Huerbin, Amy Klym, Linda McHugh, Cynthia Sobran, J. Ken Leader, Bin Zheng, David Gur

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480073

KEYWORDS: Breast cancer, Breast, Diagnostics, Medical imaging, Lithium, Mammography, Ultrasonography, Biopsy, Algorithm development, Signal processing

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Proceedings Article | 19 May 2003 Paper

Multi-site telemammography system: preliminary assessment of technical and operational issues

John Drescher, Glenn Maitz, Christopher Traylor, J. Ken Leader, Ronald Clearfield, Ratan Shah, Marie Ganott, Francine Pugliese, Dian Duffner, Janet Lockhart, David Gur

Proceedings Volume 5033, (2003) https://doi.org/10.1117/12.480452

KEYWORDS: Image compression, Telecommunications, Image processing, Local area networks, Mammography, Data communications, Tissues, Image display, Image quality, Breast

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Proceedings Article | 15 May 2003 Paper

A simple method for automated lung segmentation in x-ray CT images

Bin Zheng, J. Ken Leader, Glenn Maitz, Brian Chapman, Carl Fuhrman, Robert Rogers, Frank Sciurba, Andrew Perez, Paul Thompson, Walter Good, David Gur

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480290

KEYWORDS: Lung, Image segmentation, Computed tomography, X-ray computed tomography, Visualization, Image processing, Emphysema, Image filtering, X-ray imaging, X-rays

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Proceedings Article | 15 May 2003 Paper

Improving CAD performance in detecting masses depicted on prior images

Bin Zheng, Xiao Hui Wang, Luisa Wallace, Cathy Cohen, Lara Hardesty, Christiane Hakim, Gordon Abran, Jules Sumkin, David Gur

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480291

KEYWORDS: Computer aided design, Databases, Computer aided diagnosis and therapy, Computer simulations, Mammography, Artificial neural networks, Cancer, Feature extraction, Biopsy

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Proceedings Article | 16 May 2002 Paper

Design considerations for a multisite POTS-based telemammography system

John Drescher, Glenn Maitz, J. Ken Leader, Jules Sumkin, William Poller, Herta Klaman, Bin Zheng, David Gur

Proceedings Volume 4685, (2002) https://doi.org/10.1117/12.467035

KEYWORDS: Telecommunications, Image compression, Image transmission, Mammography, Computer aided design, Data processing, Image segmentation, Image processing, Breast cancer, Data compression

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Proceedings Article | 9 May 2002 Paper

Change of region conspicuity in bilateral mammograms: potential impact on CAD performance

Bin Zheng, Xiao Hui Wang, Yuan-Hsiang Chang, Lara Hardesty, Marie Ganott, Walter Good, David Gur

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467217

KEYWORDS: Skin, Nipple, Chest, Mammography, Computer aided diagnosis and therapy, Image registration, Computer aided design, Algorithm development, Tissues, Breast

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Proceedings Article | 9 May 2002 Paper

Incorporation of negative regions in a knowledge-based computer-aided detection scheme

Yuan-Hsiang Chang, Xiao Hui Wang, Lara Hardesty, Christiane Hakim, Bin Zheng, Walter Good, David Gur

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467215

KEYWORDS: Mammography, Computer aided design, Computer aided diagnosis and therapy, Image processing, Information fusion, Breast cancer, Tissues, Feature extraction, Radiology

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Proceedings Article | 12 April 2002 Paper

Detecting abnormalities in noncued areas of digitized mammograms: an observer experience

William Poller, Bin Zheng, Jules Sumkin, David Gur

Proceedings Volume 4686, (2002) https://doi.org/10.1117/12.462665

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Environmental sensing, Radiology, Biopsy, Cancer, Chest, Computer aided design, Psychology, Diagnostics

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Proceedings Article | 12 April 2002 Paper

Identification of superior discriminations during ROC studies

Amy Klym, James Ferris, Joan Lacomis, Sara Golla, Thomas Warfel, Barry McCook, Peter Davis, David Gur

Proceedings Volume 4686, (2002) https://doi.org/10.1117/12.462699

KEYWORDS: Imaging systems, Analytical research, Research facilities, Argon, Statistical analysis, Radiology, Diagnostics, Imaging technologies, Receivers, Astatine

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Proceedings Article | 12 April 2002 Paper

Identification of superior discriminators during non-ROC studies

Jill King, F. Leland Thaete, Jules Sumkin, Cynthia Britton, Arleen Peterson, Jeffrey Towers, Thomas Chang, Carl Fuhrman, David Gur

Proceedings Volume 4686, (2002) https://doi.org/10.1117/12.462700

KEYWORDS: Image compression, Image quality, Chest, Image resolution, Image processing, Chromium, Statistical analysis, Image enhancement, Receivers, Visualization

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Proceedings Article | 14 April 2000 Paper

Using a constrained formulation based on probability summation to fit receiver operating characteristic (ROC) curves

Richard Swensson, Jill King, Walter Good, David Gur

Proceedings Volume 3981, (2000) https://doi.org/10.1117/12.383102

KEYWORDS: Error analysis, Statistical analysis, Medical imaging, Polonium, Data modeling, Receivers, Chest, Chest imaging, Solids, Target detection

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Proceedings Article | 24 May 1999 Paper

Gains in accuracy from averaging ratings of abnormality

Richard Swensson, Jill King, David Gur, Walter Good

Proceedings Volume 3663, (1999) https://doi.org/10.1117/12.349630

KEYWORDS: Statistical analysis, Error analysis, Statistical modeling, Chest imaging, Image compression, Data analysis, Radiology, Medical imaging, Receivers, Astatine

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Proceedings Article | 24 May 1999 Paper

Using incomplete and imprecise localization data on images to improve estimates of detection accuracy

Richard Swensson, Glenn Maitz, Jill King, David Gur

Proceedings Volume 3663, (1999) https://doi.org/10.1117/12.349665

KEYWORDS: Error analysis, Statistical analysis, Data analysis, Image analysis, Lung, Statistical modeling, Data modeling, Analytical research, Video, Chromium

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Proceedings Article | 24 May 1999 Paper

Observer performance assessment of JPEG-compressed high-resolution chest images

Walter Good, Glenn Maitz, Jill King, Rose Gennari, David Gur

Proceedings Volume 3663, (1999) https://doi.org/10.1117/12.349627

KEYWORDS: Image compression, Quantization, Image resolution, Image quality, Chest, Chromium, Image quality standards, Standards development, Signal to noise ratio, Chest imaging

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Proceedings Article | 21 May 1999 Paper

Multi-image CAD employing features derived from ipsilateral mammographic views

Walter Good, Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Glenn Maitz, David Gur

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348602

KEYWORDS: Computer aided diagnosis and therapy, Computer aided design, Mammography, Breast, Nipple, Image processing, Image compression, Image segmentation, Tissues, Algorithm development

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Proceedings Article | 21 May 1999 Paper

Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection

Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Walter Good, David Gur

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348558

KEYWORDS: Databases, Computer aided design, Computer aided diagnosis and therapy, Image segmentation, Computer networks, Mammography, Image processing, Artificial neural networks, Genetic algorithms, Breast cancer

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Proceedings Article | 24 June 1998 Paper

Assessment of mass detection using tissue background information as input to a computer-assisted diagnosis scheme

Bin Zheng, Yuan-Hsiang Chang, Walter Good, David Gur

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310888

KEYWORDS: Databases, Computer aided design, Computer aided diagnosis and therapy, Tissues, Neurons, Feature extraction, Medical imaging, Mammography, Artificial neural networks, Image processing

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Proceedings Article | 16 April 1997 Paper

Relationship of subjective ratings of image quality and observer performance

Howard Rockette, Christopher Johns, Jane Weissman, J. Michael Holbert, Jules Sumkin, Jill King, David Gur

Proceedings Volume 3036, (1997) https://doi.org/10.1117/12.271287

KEYWORDS: Image quality, Diagnostics, Chromium, Data acquisition, Radiography, Image resolution, Image processing, Receivers, Image analysis, Chest

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Proceedings Article | 17 April 1995 Paper

Spontaneous migration to a higher dose in computed radiography (CR) imaging

David Gur, Carl Fuhrman, John Feist, Robert Slifko, Barbara Peace, Chris Shaw

Proceedings Volume 2436, (1995) https://doi.org/10.1117/12.206854

KEYWORDS: Chromium, Radiography, Image quality, Chest, Image quality standards, Standards development, Imaging systems, Radiology, Gold, Physics

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Proceedings Article | 17 April 1995 Paper

Variability in reader selection of subtle cases for inclusion in a reduced size ROC study

Jill King, Howard Rockette, Melinda Staiger, Jane Weissman, Howard Eisen, Christopher Johns, David Gur

Proceedings Volume 2436, (1995) https://doi.org/10.1117/12.206855

KEYWORDS: Image storage, Image classification, Imaging systems, Image processing, Chest, Image resolution, Radiology, Receivers, Astatine, Argon

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Proceedings Article | 15 May 1994 Paper

Intrahospital teleradiology--ER to the ICU connection

Stefanie Lattner, John Herron, Carl Fuhrman, Jeffrey Towers, F. Leland Thaete, David Gur

Proceedings Volume 2165, (1994) https://doi.org/10.1117/12.174314

KEYWORDS: Radiology, Surgery, Chest imaging, Image quality, Chest, Image registration, Data storage, Image processing, Teleradiology, Image acquisition

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Proceedings Article | 15 May 1994 Paper

Data entry issues in the PACS environment

Stefanie Lattner, Walter Good, John Herron, David Gur

Proceedings Volume 2165, (1994) https://doi.org/10.1117/12.174303

KEYWORDS: Information security, Scanners, Human-machine interfaces, Computed tomography, Data acquisition, Picture Archiving and Communication System, Imaging systems, Databases, Telecommunications, Computer security

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Proceedings Article | 1 April 1994 Paper

Nonportable CR of the chest--radiologists' acceptance

Rose Gennari, David Gur, Linda Miketic, William Campbell, James Oliver, Michael Plunkett

Proceedings Volume 2166, (1994) https://doi.org/10.1117/12.171731

KEYWORDS: Chromium, Chest, Diagnostics, Radiography, Bone, Imaging systems, Tissues, Radiology, Image quality, LED displays

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Proceedings Article | 14 September 1993 Paper

Dentomaxillofacial imaging with computed-radiography techniques: a preliminary study

Chris Shaw, Stanley Kapa, Audrey Furkart, David Gur

Proceedings Volume 1896, (1993) https://doi.org/10.1117/12.154580

KEYWORDS: Surface plasmons, Image resolution, Spatial resolution, Panoramic photography, Radiography, Chromium, X-rays, Image processing, Image enhancement, Imaging systems

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Proceedings Article | 8 September 1993 Paper

Directed and nondirected AP chest in the ICU: potential added value with electronic systems

David Gur, William Straub, Rhett Lieberman, Rose Gennari, Carl Fuhrman, Jules Sumkin

Proceedings Volume 1899, (1993) https://doi.org/10.1117/12.152914

KEYWORDS: Chest, Radiology, Picture Archiving and Communication System, Chest imaging, Transplantation, Image processing, Databases, Teleradiology

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Proceedings Article | 8 September 1993 Paper

Exploring computerized mammographic reporting with feedback

Kathleen Harris, Barbara Good, Jill King, David Toma, David Gur, Zahra Ilkhanipour, Melinda Staiger, James Oliver, Phyllis Wintz, Marie Ganott, Cynthia Britton, William Straub

Proceedings Volume 1899, (1993) https://doi.org/10.1117/12.152925

KEYWORDS: Computing systems, Databases, Mammography, Breast, Diagnostics, Picture Archiving and Communication System, Binary data, Neural networks, Biopsy, Radiology

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Proceedings Article | 8 September 1993 Paper

Device and system performance expectations in clinical PACS

John Herron, Stefanie Lattner, Jill King, David Gur, James Oliver, Michael Plunkett, Jeffrey Towers

Proceedings Volume 1899, (1993) https://doi.org/10.1117/12.152927

KEYWORDS: Picture Archiving and Communication System, Radiology, Imaging systems, Databases, Medicine, Image quality, Human-machine interfaces, System integration, Telecommunications

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Proceedings Article | 8 September 1993 Paper

Intrahospital teleradiology from the emergency room

Carl Fuhrman, B. Slasky, David Gur, Stefanie Lattner, John Herron, Michael Plunkett, Jeffrey Towers, F. Leland Thaete

Proceedings Volume 1899, (1993) https://doi.org/10.1117/12.152907

KEYWORDS: Diagnostics, Teleradiology, Image processing, Picture Archiving and Communication System, Image quality, Nonimpact printing, Radiology, System identification, X-ray imaging, X-rays

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Proceedings Article | 1 July 1992 Paper

Concept and design considerations for an electronic film library

Walter Good, Catherine Burzik, Paul Scanlon, Glenn Maitz, John Herron, David Gur

Proceedings Volume 1654, (1992) https://doi.org/10.1117/12.60272

KEYWORDS: Manufacturing, Optical fabrication, Optical character recognition, Optical discs, Computed tomography, Scanners

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Proceedings Article | 1 July 1992 Paper

Selection of `subtle` cases for ROC studies

David Gur, Jill King, Howard Rockette, Carl Fuhrman, Jules Sumkin

Proceedings Volume 1654, (1992) https://doi.org/10.1117/12.60283

KEYWORDS: Imaging systems

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Proceedings Article | 1 June 1992 Paper

Pixel averaging versus digitization using larger apertures: a comparison of the spatial resolution properties

Chris Shaw, John Herron, David Gur

Proceedings Volume 1651, (1992) https://doi.org/10.1117/12.59390

KEYWORDS: Image filtering, Spatial resolution, Signal processing, Image resolution, Image processing, Modulation transfer functions, Chest imaging, Lead, Optical filters, Medical imaging

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Proceedings Article | 1 June 1992 Paper

Comparison of three different schemes for dual-energy subtraction imaging in digital radiography: a signal-to-noise analysis

Chris Shaw, David Gur

Proceedings Volume 1651, (1992) https://doi.org/10.1117/12.59389

KEYWORDS: Sensors, X-rays, Tissues, Bone, Copper, X-ray detectors, Signal to noise ratio, X-ray imaging, Dual energy imaging, Interference (communication)

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Proceedings Article | 1 June 1992 Paper

Quantization techniques for the compression of chest images by JPEG-type algorithms

Walter Good, David Gur

Proceedings Volume 1652, (1992) https://doi.org/10.1117/12.59418

KEYWORDS: Quantization, Image compression, Image filtering, Chest, Image processing, Image quality standards, Medical imaging, Nonlinear filtering, Computer programming, Image quality

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Proceedings Article | 1 June 1992 Paper

Signal fading, erasure, and rescan in storage phosphor imaging

Chris Shaw, John Herron, David Gur

Proceedings Volume 1651, (1992) https://doi.org/10.1117/12.59393

KEYWORDS: X-rays, Radiography, Signal to noise ratio, Image processing, Medical imaging, X-ray imaging, Radiology, Image storage, Imaging systems, Signal processing

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Proceedings Article | 1 May 1992 Paper

Simultaneous and sequential display of ICU AP-chest images

David Gur, Walter Good, Jill King

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59495

KEYWORDS: Chest, Image quality, Diagnostics, Radiography, Image resolution, Modulation transfer functions, Image display, Radiology, Displays, Computed tomography

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