Dr. Benjamin L. Odry Profile

Dr. Benjamin L. Odry

Research Scientist at Siemens Corp, Corporate Technology

SPIE Involvement:

Author

Publications (17)

Proceedings Article | 13 March 2013 Paper

Paper

Pulmonary lobe segmentation using the thin plate spline (TPS) with the help of the fissure localization areas

Benjamin Odry, Pauline Steininger, Li Zhang, Atilla Kiraly, Alexander Mahnke, Sandra Sudarsky, Carol Novak, Bernhard Geiger, Shuping Qing, Hendrik Ditt

Proceedings Volume 8669, 86690X (2013) https://doi.org/10.1117/12.2007098

KEYWORDS: Lung, Image segmentation, Computed tomography, Detection and tracking algorithms, Plutonium, Image processing, Ions, Picosecond phenomena, Lithium, Scanners

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Proceedings Article | 12 March 2013 Paper

Paper

A knowledge-driven quasi-global registration of thoracic-abdominal CT and CBCT for image-guided interventions

Li Zhang, Christophe Chefd'hotel, Vincent Ordy, Jie Zheng, Xiang Deng, Benjamin Odry

Proceedings Volume 8671, 867110 (2013) https://doi.org/10.1117/12.2007747

KEYWORDS: Image registration, Acquisition tracking and pointing, Optimization (mathematics), 3D image processing, Computed tomography, 3D acquisition, Image-guided intervention, Spine, Rigid registration, Optical spheres

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Proceedings Article | 23 February 2012 Paper

Paper

Comparison of analysis methods for airway quantification

Benjamin Odry, Atilla Kiraly, Carol Novak, David Naidich

Proceedings Volume 8315, 83152R (2012) https://doi.org/10.1117/12.912420

KEYWORDS: Image segmentation, Arteries, Analytical research, Chronic obstructive pulmonary disease, Calibration, Computed tomography, Image filtering, Lithium, Visualization, Scanners

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Proceedings Article | 9 March 2010 Paper

Paper

Quantitative evaluation of bronchial enhancement: preliminary observations

Benjamin Odry, Atilla Kiraly, Carol Novak, David Naidich, Myrna C. Godoy, Bernhard Schmidt

Proceedings Volume 7626, 76260Z (2010) https://doi.org/10.1117/12.844491

KEYWORDS: Iodine, Inflammation, Dual energy imaging, Lung, Data acquisition, Chronic obstructive pulmonary disease, Computed tomography, Arteries, Visualization, Image registration

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Proceedings Article | 28 February 2009 Paper

Paper

Evaluation of scoring accuracy for airway wall thickness

Benjamin Odry, Atilla Kiraly, Carol Novak, David Naidich, Jane Ko, Myrna C. Godoy

Proceedings Volume 7260, 72602X (2009) https://doi.org/10.1117/12.811910

KEYWORDS: Visualization, Arteries, Optical inspection, Computed tomography, Computing systems, Image segmentation, Chronic obstructive pulmonary disease, Image analysis, Scanners, Inspection

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Bronchial wall thickening is commonly observed in airway diseases. One method often used to quantitatively evaluate wall thickening in CT images is to estimate the ratio of the bronchial wall to the accompanying artery, or BWA ratio, and then assign a severity score based on the ratio. Assessment by visual inspection is unfortunately limited to airways perpendicular or parallel to the scanning plane. With high-resolution images from multi-detector CT scanners, it becomes possible to assess airways in any orientation. We selected CT scans from 20 patients with mild to severe COPD. A computer system automatically segmented each bronchial tree and measured the bronchial wall thicknesses. Next, neighboring arteries were detected and measured to determine BWA ratios. A score characterizing the extent and severity of wall thickening within each lobe was computed according to recommendations by Sheehan et al [1]. Two experienced radiologists independently scored wall thickening using visual assessment. Spearman's rank correlation showed a non-significant negative correlation (r=-0.1) between the computer and the reader average (p=0.4), while the correlation between readers was significant at r=0.65 (p=0.001). We subsequently identified 24 lobes with high discrepancies between visual and automated scoring. The readers re-examined those lobes and measured wall thickness using electronic calipers on perpendicular cross sections, rather than visual assessment. Using this more objective standard of wall thickness, the reader estimates of wall thickening increased to reach a significant positive correlation with automated scoring of r=0.65 (p=0.001). These results indicate that subjectivity is an important problem with visual evaluation, and that visual inspection may frequently underestimate disease extent and severity. Given that a manual evaluation of all airways is infeasible in routine clinical practice, we argue that automated methods should be developed and utilized.

Proceedings Article | 28 February 2009 Paper

Paper

Automated detection of presence of mucus foci in airway diseases: preliminary results

Benjamin Odry, Atilla Kiraly, Carol Novak, David Naidich, Jane Ko, Myrna C. Godoy

Proceedings Volume 7260, 72603B (2009) https://doi.org/10.1117/12.811923

KEYWORDS: Lung, Image segmentation, Visualization, Reconstruction algorithms, Chronic obstructive pulmonary disease, Optical inspection, Computed tomography, Detection and tracking algorithms, Cystic fibrosis, Binary data

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Proceedings Article | 17 March 2008 Paper

Paper

An evaluation of automated broncho-arterial ratios for reliable assessment of bronchiectasis

Benjamin Odry, Atilla Kiraly, Carol Novak, David Naidich, Jean-Francois Lerallut

Proceedings Volume 6915, 69152M (2008) https://doi.org/10.1117/12.772579

KEYWORDS: Arteries, Computed tomography, Image segmentation, Computer aided diagnosis and therapy, Diagnostics, Computer simulations, Solid modeling, Medical imaging, Lung, Classification systems

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Proceedings Article | 12 March 2008 Paper

Paper

Active contour approach for accurate quantitative airway analysis

Benjamin Odry, Atilla Kiraly, Greg Slabaugh, Carol Novak, David Naidich, Jean-Francois Lerallut

Proceedings Volume 6916, 691613 (2008) https://doi.org/10.1117/12.772592

KEYWORDS: Image segmentation, Arteries, Computed tomography, Point spread functions, Lung, Diagnostics, Medical imaging, Calibration, Sensors, Information operations

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Proceedings Article | 10 May 2007 Paper

Paper

Automated detection of mucus plugs within bronchial tree in MSCT images

Benjamin Odry, Diran Guiliguian, Atilla Kiraly, Carol Novak, David Naidich, Jean-Francois Lerallut

Proceedings Volume 6511, 651110 (2007) https://doi.org/10.1117/12.709783

KEYWORDS: Image processing, Image segmentation, Tolerancing, Inflammation, Computed tomography, Shape analysis, Lung, Optical spheres, Algorithm development, Chronic obstructive pulmonary disease

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Proceedings Article | 26 March 2007 Paper

Paper

Solid component evaluation in mixed ground glass nodules

Benjamin Odry, Jing Huo, Li Zhang, Carol Novak, David Naidich

Proceedings Volume 6512, 65120R (2007) https://doi.org/10.1117/12.709892

KEYWORDS: Solids, Image segmentation, Glasses, Lung, 3D modeling, Computed tomography, Lithium, Radiology, Image processing, Algorithm development

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Proceedings Article | 13 March 2006 Paper

Paper

Automated airway evaluation system for multi-slice computed tomography using airway lumen diameter, airway wall thickness and broncho-arterial ratio

Benjamin Odry, Atilla Kiraly, Carol Novak, David Naidich, Jean-Francois Lerallut

Proceedings Volume 6143, 61430Q (2006) https://doi.org/10.1117/12.653796

KEYWORDS: Image segmentation, Arteries, Computed tomography, Visualization, Computing systems, Optical inspection, 3D visualizations, Emphysema, Inspection, Lung

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Proceedings Article | 10 March 2006 Paper

Paper

Robust local intervertebral disc alignment for spinal MRI

James Reisman, Jan Höppner, Szu-Hao Huang, Li Zhang, Shang-Hong Lai, Benjamin Odry, Carol Novak

Proceedings Volume 6144, 614406 (2006) https://doi.org/10.1117/12.652311

KEYWORDS: Spine, Magnetic resonance imaging, Image segmentation, Diagnostics, Detection and tracking algorithms, Automatic alignment, Scanners, Binary data, Magnetism, Reliability

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Proceedings Article | 29 April 2005 Paper

Paper

The effects of 3D region-based compression on the performance of an automatic lung nodule detection system

Benjamin Odry, Karthik Krishnan, Mariappan Nadar, David Naidich, Carol Novak, Michael Marcellin

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595475

KEYWORDS: Lung, Image compression, Computer aided design, Image segmentation, Detection and tracking algorithms, JPEG2000, Computer aided diagnosis and therapy, Computed tomography, Medical imaging, Image processing algorithms and systems

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There is growing interest in computer aided diagnosis applications including automatic detection of lung nodules from multislice computed tomography (CT). However the increase in the number and size of CT datasets introduces high costs for data storage and transmission, and becomes an obstacle to routine clinical exam as well as hindering widespread utilization of computerized applications. We investigated the effects of 3D lossy region-based JPEG2000 standard compression on the results of an automatic lung nodule detection system. As the algorithm detects the lungs within the datasets, we used this lung segmentation to define a region of interest (ROI) where the compression should be of higher fidelity. We tested 4 methods of 3D compression: 1) default compression of the whole image, 2) default compression of segmented lungs with masking out all non-lung regions, 3) ROI-based compression as specified in the JPEG2000 standard and 4) compression where voxels in the ROI are weighted to be given emphasis in the encoding. We tested 7 compression ratios per method: 1, 4, 6, 8, 10, 20, and 30 to 1. We then evaluated our experimental CAD algorithm on 10 patients with 67 documented nodules initially identified on the decompressed data. Sensitivities and false positive rates were compared for the various compression methods and ratios. We found that region-based compression generally performs better than default compression. The sensitivity with default compression decreased from 85% at no compression to 61% at 30:1 compression, a decrease of 25%, whereas the masked compression method saw a decreased in sensitivity on only 13.5% at maximum compression. At compression levels up to 10:1, all 3 region-based compression methods had decreases in sensitivity of 7.5% or less. Detection of small nodules (< 4mm in diameter) was more affected by compression than detection of large nodules; sensitivity to calcified nodules was less affected by compression than to non-calcified nodules.

Proceedings Article | 12 May 2004 Paper

Paper

A new algorithm for local surface smoothing with application to chest wall nodule segmentation in lung CT data

Hong Shen, Bernhard Goebel, Benjamin Odry

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535679

KEYWORDS: Chest, Image segmentation, Lung, Lung cancer, Cancer, Colorectal cancer, Computed tomography, Medical imaging, Pathology, 3D image processing

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Proceedings Article | 12 May 2004 Paper

Paper

System for automatic detection of lung nodules exhibiting growth

Carol Novak, Hong Shen, Benjamin Odry, Jane Ko, David Naidich

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535389

KEYWORDS: Lung, Lung cancer, Chest, Computed tomography, Detection and tracking algorithms, Tissues, Feature extraction, Cancer, Computer aided diagnosis and therapy, Solids

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Proceedings Article | 9 May 2002 Paper

Paper

Knowledge-based automatic detection of multitype lung nodules from multidetector CT studies

JianZhong Qian, Li Fan, Guo-Qing Wei, Carol Novak, Benjamin Odry, Hong Shen, Li Zhang, David Naidich, Jane Ko, Ami Rubinowitz, Georgeann McGuinness, Gerhard Kohl, Ernst Klotz

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467211

KEYWORDS: Lung, Chest, Feature extraction, Detection and tracking algorithms, Computed tomography, Algorithm development, Image processing, Opacity, Medical imaging, Dimension reduction

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Proceedings Article | 9 May 2002 Paper

Paper

Automatic segmentation of pulmonary nodules by using dynamic 3D cross-correlation for interactive CAD systems

Li Fan, JianZhong Qian, Benjamin Odry, Hong Shen, David Naidich, Gerhard Kohl, Ernst Klotz

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467100

KEYWORDS: Image segmentation, Lung cancer, Lung, Computed tomography, Chest, X-ray computed tomography, 3D image processing, Medical imaging, CAD systems, Surgery

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Showing 5 of 17 publications

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