Dr. Shih-Chung B. Lo Profile

Dr. Shih-Chung B. Lo

Professor at Georgetown Univ Medical Ctr

SPIE Involvement:

Author

Publications (75)

Proceedings Article | 19 March 2013 Paper

Image reconstruction of arc cone-beam CT with reprojection: a preliminary study

Shih-Chung Lo, Matthew Freedman

Proceedings Volume 8668, 866831 (2013) https://doi.org/10.1117/12.2008156

KEYWORDS: X-rays, Computed tomography, Head, X-ray computed tomography, Glasses, X-ray imaging, Image restoration, CT reconstruction, Reconstruction algorithms, 3D image reconstruction

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Proceedings Article | 23 February 2012 Paper

Changes of nodule detection after radiologists read bone opacity suppressed chest radiography

Matthew Freedman, Shih-Chung Lo

Proceedings Volume 8315, 83152S (2012) https://doi.org/10.1117/12.911651

KEYWORDS: Bone, Opacity, Cancer, Lung cancer, Chest imaging, Lung, Chest, Radiography, Colorectal cancer, Ovarian cancer

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Proceedings Article | 9 March 2011 Paper

BI-RADS guided mammographic mass retrieval

Yimo Tao, Lubomir Hadjiski, Heang-Ping Chan, Matthew Freedman, Shih-Chung Lo

Proceedings Volume 7963, 79632H (2011) https://doi.org/10.1117/12.878274

KEYWORDS: Image retrieval, Feature extraction, Mammography, Visualization, Computer aided diagnosis and therapy, Computing systems, Image segmentation, Content based image retrieval, Biological research, Biopsy

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Proceedings Article | 9 March 2010 Paper

Joint segmentation and spiculation detection for ill-defined and spiculated mammographic masses

Matthew Freedman, Jianhua Xuan, Yimo Tao, Shih-Chung Lo

Proceedings Volume 7624, 762407 (2010) https://doi.org/10.1117/12.844045

KEYWORDS: Image segmentation, Phase modulation, Image processing algorithms and systems, Tissues, Detection and tracking algorithms, Image enhancement, Ridge detection, Expectation maximization algorithms, Breast, Binary data

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Proceedings Article | 21 March 2009 Paper

PE-CMOS-based C-mode ultrasound: signal acquisition and time gating

Matthew Freedman, Marvin Lasser, Yue Joseph Wang, Seong-Ki Mun, Chu-Chuan Liu, John Kula, Bob Lasser, Shih-Chung B. Lo

Proceedings Volume 7265, 726508 (2009) https://doi.org/10.1117/12.811800

KEYWORDS: Ultrasonography, Bone, Picosecond phenomena, Sensors, Prototyping, Skin, Image sensors, Signal detection, Systems modeling, Image acquisition

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Proceedings Article | 17 March 2008 Paper

Automatic categorization of mammographic masses using BI-RADS as a guidance

Jianhua Xuan, Shih-Chung Lo, Erini Makariou, Yimo Tao, Matthew Freedman

Proceedings Volume 6915, 691526 (2008) https://doi.org/10.1117/12.772808

KEYWORDS: Image retrieval, Feature extraction, Image filtering, Gaussian filters, Mammography, Breast, Associative arrays, Electronic filtering, Cancer, Diagnostics

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Proceedings Article | 10 March 2008 Paper

Projection-reflection ultrasound images using PE-CMOS sensor: a preliminary bone fracture study

Bob Lasser, Seong-Ki Mun, Matthew Freedman, Marvin Lasser, Yue Joseph Wang, John Kula, Chu-Chuan Liu, Shih-Chung B. Lo

Proceedings Volume 6920, 692005 (2008) https://doi.org/10.1117/12.772847

KEYWORDS: Bone, Ultrasonography, Prototyping, Sensors, Speckle, Skin, Tissues, Transducers, Signal detection, Acoustics

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Proceedings Article | 31 March 2007 Paper

A preliminary study of content-based mammographic masses retrieval

Shih-Chung Lo, Matthew Freedman, Jianhua Xuan, Yimo Tao

Proceedings Volume 6514, 65141Z (2007) https://doi.org/10.1117/12.711528

KEYWORDS: Feature extraction, Databases, Data modeling, Image retrieval, Computer aided diagnosis and therapy, Computing systems, Mammography, Visualization, Medical imaging, Fourier transforms

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Proceedings Article | 12 March 2007 Paper

Quantitative ultrasound images generated by a PE-CMOS sensor array: scatter modeling and image restoration

Bob Lasser, Marvin Lasser, Shih-Chung Ben Lo, Matthew Freedman, Yue Joseph Wang, Chu-Chuan Liu, John Kula

Proceedings Volume 6513, 65130Q (2007) https://doi.org/10.1117/12.712224

KEYWORDS: Ultrasonography, Scattering, Sensors, Laser scattering, Collimation, Image processing, Prototyping, Image restoration, Signal attenuation, Acoustics

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Proceedings Article | 17 March 2006 Paper

Mammographic CADx system using an image library with an intelligent agent: a pattern matching approach

Erini Makariou, Shih-Chung Lo, Lisa Kinnard, Matthew Freedman

Proceedings Volume 6144, 61445O (2006) https://doi.org/10.1117/12.654667

KEYWORDS: Image segmentation, Computer aided diagnosis and therapy, Mammography, Breast cancer, Image retrieval, Diagnostics, Breast, Fuzzy logic, Medical imaging, Computing systems

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Proceedings Article | 16 March 2006 Paper

Investigation of foreign objects in soft-tissue using a PE-CMOS ultrasound system: a preliminary comparative study

Chu-Chuan Liu, John Kula, Shih-Chung Ben Lo, Bob Lasser, Tsung-I Hsieh, Matthew Freedman, Yue Joseph Wang, Marvin Lasser, Anita Sarcone

Proceedings Volume 6147, 61470K (2006) https://doi.org/10.1117/12.654105

KEYWORDS: Ultrasonography, X-ray imaging, X-rays, Imaging systems, Aluminum, Tissues, Chromium, Glasses, Computing systems, Sensors

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Proceedings Article | 29 April 2005 Paper

Mass segmentation of dense breasts on digitized mammograms: analysis of a probability-based function

Teresa Osicka, Erini Makariou, Lisa Kinnard, Eva Duckett, Matthew Freedman, Mohamed Chouikha, Shih-Chung Lo

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594842

KEYWORDS: Image segmentation, Breast, Mammography, Tissues, Radiology, Statistical analysis, Cancer, Diagnostics, Computer aided diagnosis and therapy, Visualization

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Proceedings Article | 12 April 2005 Paper

Transmission and reflective ultrasound images using PE-CMOS sensor array

Yue Wang, Matthew Freedman, Bob Lasser, Shih-Chung Lo, Marvin Lasser, John Kula, Chu Chuan Liu

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.595615

KEYWORDS: Ultrasonography, Reflectivity, Sensors, Signal attenuation, Transducers, Tissues, Bone, Modulation transfer functions, Signal detection, Imaging systems

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Proceedings Article | 6 April 2005 Paper

Computer aided detection of lung cancer in the absence of the cancer on chest radiographs: effect of the computer-aided detection on radiologists’ performance on cancer-free cases

Matthew Freedman, Fleming Lure, Teresa Osicka, Hui Zhao, Xin-Wei Xu, Shih-Chung Lo, Jesse Lin, Ron Zhang

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.594568

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Lung cancer, Chest imaging, Clinical trials, CAD systems, Switches, Statistical analysis, Radiography

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Using data from a clinical trial of a commercial CAD system for lung cancer detection we separately analyzed the location, if any, selected on each film by 15 radiologists as they interpreted chest radiographs, 160 of which did not contain cancers. On the cancer-free cases, the radiologists showed statistically significant difference in decisions while using the CAD (p-value 0.002). Average specificity without computer assistance was 78%, and with computer assistance 73%. In a clinical trial with CAD for lung cancer detection there are multiple machine false positives. On chest radiographs of older current or former smokers, there are many scars that can appear like cancer to the interpreting radiologists. We are reporting on the radiologists' false positives and on the effect of machine false positive detections on observer performance on cancer-free cases. The only difference between radiologists occurred when they changed their initial true negative decision to false positive (p-value less than 0.0001), average confidence level increased, on the scale from 0.0 to 100.0, from 16.9 (high confidence of non-cancer) to 53.5 (moderate confidence cancer was present). We are reporting on the consistency of misinterpretation by multiple radiologists when they interpret cancer-free radiographs of smokers in the absence of CAD prompts. When multiple radiologists selected the same false positive location, there was usually a definite abnormality that triggered this response. The CAD identifies areas that are of sufficient concern for cancer that the radiologists will switch from a correct decision of no cancer to mark a false positive, previously overlooked, but suspicious appearing cancer-free area; one that has often been marked by another radiologist without the use of the CAD prompt. This work has implications on what should be accepted as ground truth in ROC studies: One might ask, "What a false positive response means?" when the finding, clinically, looks like cancer-it just isn’t cancer, based on long-term follow-up or histology.

Proceedings Article | 12 May 2004 Paper

Enhancement of lung nodule detection in temporal thoracic CT

Matthew Freedman, Ruiping Li, J. Lin, Li-Yueh Hsu, Yuan Ming Lure, Hui Zhao, Shih-Chung Lo

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.537621

KEYWORDS: Computed tomography, Lung, Image registration, 3D image processing, Image segmentation, Lung cancer, Cancer, 3D vision, Chest, Signal to noise ratio

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Proceedings Article | 4 May 2004 Paper

Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD true positive/false negative detections on radiologists' confidence level

Xin-Wei Xu, Hui Zhao, Shih-Chung Benedict Lo, Teresa Osicka, Matthew Freedman, Fleming Lure, Jesse Lin, Ron Zhang

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.536157

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Lung cancer, Chest imaging, Medical imaging, Clinical trials, CAD systems

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Proceedings Article | 28 April 2004 Paper

Projection ultrasound and ultrasound CT using a PE-CMOS sensor: a preliminary study

Chu Chuan Liu, JianChao Zeng, Bob Lasser, Doug Ro, Shih-Chung Lo, Marvin Lasser, David Rich, John Kula, Matthew Freedman

Proceedings Volume 5373, (2004) https://doi.org/10.1117/12.537672

KEYWORDS: Ultrasonography, Signal attenuation, Computed tomography, Sensors, Computing systems, Breast, Scattering, 3D image processing, Acoustics, Tissues

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Proceedings Article | 5 June 2003 Paper

Phase contrast digital mammography using molybdenum x-ray: clinical implications in detectability improvement

Gale Sisney, Chika Honda, Akira Ishisaka, Erini Makariou, Fumio Shimada, Hiromu Ohara, Matthew Freedman, Edward Pien, Shih-Chung Lo

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.480366

KEYWORDS: Phase imaging, X-rays, Mammography, Phase contrast, Digital mammography, X-ray imaging, Printing, Spatial resolution, Chromium, Imaging systems

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Proceedings Article | 22 May 2003 Paper

Computer-aided detection of lung cancer on chest radiographs: differences in the interpretation time of radiologists showing versus not showing improvement with CAD

Xin-Wei Xu, Matthew Freedman, Shih-Chung Lo, Fleming Lure, Jesse Lin, Teresa Osicka, Hui Zhao, Ron Zhang

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480347

KEYWORDS: Cancer, Computer aided diagnosis and therapy, Lung cancer, Chest imaging, CAD systems, Clinical trials, Medical imaging, Diagnostics, Classification systems

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Proceedings Article | 15 May 2003 Paper

Classification of lung nodules in diagnostic CT: an approach based on 3D vascular features, nodule density distribution, and shape features

Shih-Chung Lo, Li-Yueh Hsu, Matthew Freedman, Hui Zhao, Yuan Ming Lure

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481878

KEYWORDS: Lung, Computed tomography, Image segmentation, Diagnostics, Lung cancer, Image processing, Neural networks, 3D modeling, Chest, Tumors

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Proceedings Article | 15 May 2003 Paper

Separation of malignant and benign masses using image and segmentation features

Lisa Kinnard, Shih-Chung Lo, Paul Wang, Mohamed Chouikha, Matthew Freedman

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481889

KEYWORDS: Image segmentation, Neural networks, Feature extraction, Image classification, Tumors, Image processing, Mammography, Diagnostics, Databases, Radiology

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Proceedings Article | 9 May 2002 Paper

Enhanced lung cancer detection in temporal subtraction chest radiography using directional edge filtering techniques

Matthew Freedman, Yue Wang, Shih-Chung Lo, Hui Zhao

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467212

KEYWORDS: Image filtering, Lung, Chest, Image enhancement, Lung cancer, Image processing, Radiography, Chest imaging, Image registration, CAD systems

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Proceedings Article | 9 May 2002 Paper

Separation of malignant and benign masses using maximum-likelihood modeling and neural networks

Matthew Freedman, Paul Wang, Lisa Kinnard, Shih-Chung Lo, Mohammed Chouikha

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467216

KEYWORDS: Tumors, Image segmentation, Neural networks, Breast cancer, Tumor growth modeling, Radiology, Convolution, Mammography, Diagnostics, Image filtering

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Proceedings Article | 9 May 2002 Paper

Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD false-positive locations on radiologists' behavior

Xin-Wei Xu, Shih-Chung Lo, Fleming Lure, Jesse Lin, Hui Zhao, Ron Zhang, Matthew Freedman, Teresa Osicka

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467092

KEYWORDS: Computer aided diagnosis and therapy, Lung cancer, Chest imaging, Cancer, CAD systems, Clinical trials, Computing systems, Biopsy, Medical imaging

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Proceedings Article | 12 April 2002 Paper

Independent versus sequential reading in ROC studies of computer-assist modalities

Matthew Freedman, Kunio Doi, Robert Wagner, Sergey Beiden, Xin-Wei Xu, Robert Nishikawa, Shih-Chung Lo

Proceedings Volume 4686, (2002) https://doi.org/10.1117/12.462678

KEYWORDS: Statistical analysis, Computing systems, Chest imaging, Receivers, Computer aided diagnosis and therapy, Diagnostics, Computer aided design, Medical imaging, Data modeling

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Proceedings Article | 3 July 2001 Paper

Automatic temporal subtraction of chest radiographs and its enhancement for lung cancers

Seong Mun, Matthew Freedman, Shih-Chung Lo, Hui Zhao

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431078

KEYWORDS: Signal to noise ratio, Cancer, Lung cancer, Image registration, Chest imaging, Chest, Image segmentation, Lung, Radiography, Edge detection

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Proceedings Article | 3 July 2001 Paper

Integer wavelet compression guided by a computer-aided detection system in mammography

Heang-Ping Chan, Matthew Freedman, Kevin Berbaum, Shih-Chung Lo, Andrzej Delegacz, Erini Makariou, Donald Dorfman

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431138

KEYWORDS: Mammography, Image compression, Image quality, Wavelets, Computing systems, Digital mammography, Computer aided diagnosis and therapy, Breast, Image enhancement

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Proceedings Article | 26 June 2001 Paper

Methods for identifying changes in radiologists' behavioral operating point of sensitivity-specificity trade-offs within an ROC study of the use of computer-aided detection of lung cancer

Teresa Osicka, Matthew Freedman, Xin-Wei Xu, Ron Zhang, Shih-Chung Lo, Jesse Lin, Fleming Lure

Proceedings Volume 4324, (2001) https://doi.org/10.1117/12.431187

KEYWORDS: Cancer, Computer aided diagnosis and therapy, Lung cancer, Mammography, Digital mammography, Statistical analysis, Biopsy, Diagnostics, Lead

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Proceedings Article | 30 May 2001 Paper

C-scan transmission ultrasound based on a hybrid microelectronic sensor array and its physical performance

Matthew Freedman, Hui Zhao, Marvin Lasser, John Kula, Bob Lasser, Shih-Chung Lo, David Rich

Proceedings Volume 4325, (2001) https://doi.org/10.1117/12.428185

KEYWORDS: Ultrasonography, Optical spheres, Breast, Tumors, Biopsy, Sensors, Acoustics, Tissues, Microelectronics, X-rays

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Proceedings Article | 28 May 2001 Paper

Computer-aided detection of lung cancer on chest radiographs: algorithm performance vs. radiologists' performance by size of cancer

Ron Zhang, Hui Zhao, Shih-Chung Lo, Jesse Lin, Fleming Lure, Teresa Osicka, Xin-Wei Xu, Matthew Freedman

Proceedings Volume 4319, (2001) https://doi.org/10.1117/12.428048

KEYWORDS: Cancer, Computer aided diagnosis and therapy, Lung cancer, Chest imaging, Remote sensing, Computing systems, Detection and tracking algorithms, System integration, CAD systems

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Proceedings Article | 28 May 2001 Paper

Feature extraction, analysis, and 3D visualization of local lung regions in volumetric CT images

Matthew Freedman, Andrzej Delegacz, Seong Mun, Shih-Chung Lo

Proceedings Volume 4319, (2001) https://doi.org/10.1117/12.428121

KEYWORDS: Lung, Feature extraction, Computed tomography, Visualization, Lung cancer, 3D displays, Image segmentation, Computing systems, 3D visualizations, Volume rendering

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Proceedings Article | 6 June 2000 Paper

Computer-based decision support system: visual mapping of featured database in computer-aided diagnosis

Lan Luo, Matthew Freedman, Zuyi Wang, Shih-Chung Lo, Yue Wang

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387618

KEYWORDS: Data modeling, Visualization, Expectation maximization algorithms, Databases, Visual process modeling, Computer aided diagnosis and therapy, Principal component analysis, Feature extraction, Mammography, CAD systems

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Proceedings Article | 18 April 2000 Paper

Three-dimensional visualization system as an aid for lung cancer detection

Jae Jeong Choi, Huchen Xie, Matthew Freedman, Andrzej Delegacz, Shih-Chung Lo

Proceedings Volume 3976, (2000) https://doi.org/10.1117/12.383066

KEYWORDS: Lung, Image segmentation, Visualization, Computed tomography, 3D image processing, Volume rendering, Lung cancer, Image filtering, 3D visualizations, Opacity

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The purpose of this work was to create a 3D visualization system to aid physicians in observing abnormalities of the human lungs. A series of 20-30 helical CT lung slice images obtained from the lung cancer screening protocol as well as a series of 100-150 diagnostic helical CT lung slice images were used as an input. We designed a segmentation filter to enhance the lung boundaries and filter out small and medium bronchi from the original images. The pairs of original and filtered images were further processed with the contour extraction method to segment out only the lung field for further study. In the next step the segmented lung images containing the small bronchi and lung textures were used to generate the volumetric dataset input for the 3D visualization system. Additional processing for the extracted contour was used to smooth the 3D lung contour in the lung boundaries. The computer program developed allows, among others, viewing of the 3D lung object from various angles, zooming in and out as well as selecting the regions of interest for further viewing. The density and gradient opacity tables are defined and used to manipulate the displayed contents of 3D rendered images. Thus, an effective 'see-through' technique is applied to the 3D lung object for better visual access to the internal lung structures like bronchi and possible cancer masses. These and other features of the resulting 3D lung visualization system give the user a powerful tool to observe and investigate the patient's lungs. The filter designed for this study is a completely new solution that greatly facilitates the boundary detection. The developed 3D visualization system dedicated from chest CT provides the user a new way to explore effective diagnosis of potential lung abnormalities and cancer. In the authors' opinion, the developed system can be successfully used to view and analyze patient's lung CT images in a new powerful approach in both diagnosis and surgery-planning applications. Additionally, we see the possibility of using the system for teaching anatomy as well as pathology of the human lung.

Proceedings Article | 21 May 1999 Paper

Feature analysis of lung nodules using sector geometry and multiple circular path neural network

Jyh-Shyan Lin, Xin-Wei Xu, Andrzej Delegacz, Matthew Freedman, Shih-Chung Lo

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348564

KEYWORDS: Neural networks, Lung, Chest imaging, Image filtering, Pattern recognition, Feature extraction, Lung cancer, Cancer, Computer aided diagnosis and therapy, Radiography

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In this study, each chest radiograph was processed by a three- dimensional Gaussian-like matched filter. Edge tracking and region growing techniques were applied to the filtered image to segment all possible nodules. The area and its boundary were then divided into 36 sectors (i.e., 10 degrees per sector) using 36 equi-angle dividers radiated from the center. For each suspicious area, we computed radius, average gradient within the sector, average gradient near the boundary, and contrast were computed features within each 10 degree sector. A total of 144 computed features for one suspicious area were used as input values for a newly designed three layer neural network to perform pattern recognition studies. The neural network system was constructed to emphasize the correlation information associated with the features. In this part of the research, several circular path neural network connections between the input and the first hidden layers were linked. These included (1) self correlation networking and (2) neighborhood correlation networking. The networks for self correlation and neighborhood correlation were designed to extract the common factors within the sector and between sectors, respectively. In this study, neighborhood correlation across sectors of 20 degrees, 30 degrees, 40 degrees, and 50 degrees were used. We have tested this approach on the JPST chest radiograph database consisting of 154 chest radiographs using the grouped jack-knife method. The performance in detecting medium-sized nodules was 75% in sensitivity at 5.9 false-positives per image. The performance remained the same for large nodules (with 75% sensitivity at 5.6 false-positive per image). This work presents a new and effective way in analyzing tumor objects. Instead of lumping global features for each object and analyzing them by a conventional classifier, the new method computes features in sectors and analyzes them using a fan-oriented neural network. We also found that the MCPNN technique performs slightly more effective in detecting larger nodules than smaller nodules.

Proceedings Article | 24 June 1998 Paper

Detection of mammographic masses using sector features with a multiple-circular-path neural network

Akira Hasegawa, Matthew Freedman, Yue Wang, Shih-Chung Lo, Seong Mun, Huai Li

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310848

KEYWORDS: Neural networks, Mammography, Breast, Breast cancer, Feature extraction, Convolution, Tissues, Pattern recognition, Signal detection, Evolutionary algorithms

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Proceedings Article | 22 May 1997 Paper

Effects of motion-JPEG compression on the diagnostic quality of pediatric echocardiograms

John Cockerham, Sharon Karr, Robert Rainer, William Chimiak, Lorraine Schratz, Jeanne Baffa, Nassib Khanafer, Shih-Chung Lo, Karen Kuehl, Walid Tohme, Seong Mun, Wendelin Hayes

Proceedings Volume 3035, (1997) https://doi.org/10.1117/12.274589

KEYWORDS: Diagnostics, Heart, Doppler effect, Arteries, Telecardiology, Video compression, Video, Gold, Surgery, Networks

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Proceedings Article | 7 May 1997 Paper

Dyadic decomposition: a unified perspective on predictive, subband, and wavelet transforms

Huai Li, Seong Mun, Shih-Chung Lo, Matthew Freedman, Jian Hua Xuan, Yue Wang

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273906

KEYWORDS: Wavelets, Wavelet transforms, Data compression, Lens design, Convolution, Image compression, Antimony, Data processing, Lithium, Digital filtering

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Proceedings Article | 25 April 1997 Paper

Classification of false-positive findings on computer-aided detection of breast microcalcifications

Ivan Lau, Matthew Freedman, Shih-Chung Lo, Dorothy Artz, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274173

KEYWORDS: Computer aided diagnosis and therapy, Mammography, Detection and tracking algorithms, Breast, Computing systems, Image analysis, Neural networks, Image quality, Software

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Proceedings Article | 25 April 1997 Paper

Mammographic mass detection by stochastic modeling and a multimodular neural network

Shih-Chung Lo, Huai Li, Yue Wang, Seong Mun, Matthew Freedman

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274134

KEYWORDS: Image segmentation, Mammography, Image enhancement, Feature extraction, Neural networks, Statistical modeling, Stochastic processes, Databases, Tissues, Biopsy

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Proceedings Article | 16 April 1997 Paper

ROC study of screen-film mammography and storage phosphor digital mammography: analysis of nonconcordant classifications and implications for the approval of digital mammography systems

Rebecca Zuurbier, Hamid Jafroudi, Seong Mun, Matthew Freedman, Jacquelyn Hogge, Shih-Chung Lo, Dorothy Artz

Proceedings Volume 3036, (1997) https://doi.org/10.1117/12.271303

KEYWORDS: Digital mammography, Mammography, Cancer, Biopsy, Gold, Breast, Image processing, Diagnostics, Classification systems, Visibility

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SPIE Journal Paper | 2 April 1997 Open Access

Automatic threshold selection using histogram quantization

Yue Wang, Tulay Adali, Shih-Chung Lo

JBO, Vol. 2, Issue 02, (April 1997) https://doi.org/10.1117/12.10.1117/12.268965

KEYWORDS: Quantization, Image segmentation, Distortion, Brain, Expectation maximization algorithms, Neuroimaging, Tissues, Magnetic resonance imaging, Curium, Image analysis

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Proceedings Article | 16 April 1996 Paper

Classification of microcalcifications in digital mammograms for the diagnosis of breast cancer

Seong Mun, Akira Hasegawa, Matthew Freedman, Chris Wu, Shih-Chung Lo, Osamu Tsujii

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237985

KEYWORDS: Distance measurement, Neurons, Neural networks, Mammography, Databases, Feature selection, Breast cancer, Image restoration, Image processing, Einsteinium

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We are developing a computer program for automated classification of clustered microcalcifications associated with benign and malignant processes in mammograms. Accurate classification of microcalcifications into benign and malignant groups would help improve diagnosis sensitivity as well as reduce the number of unnecessary biopsies. In this study we investigate the effectiveness of several image features and the proposed neural network, the Trend-Oriented Radial Basis Function (TRBF). Our database is composed of 47 benign and 81 malignant region of interest (ROI) images, a total of 128 ROIs, which are selected from 50 micron by 50 micron digitized whole mammograms manually. Each 256 by 256 pixel ROI image contains clustered microcalcifications. First we extract 16 image features which are calculated from a binarized microcalcifications image and its two morphological dilation images. These features are based on 3 morphological criteria: (1) size and/or shape of the calcifications, (2) size or shape of the 'cluster,' and (3) number of microcalcifications. Secondly, we apply Karhunen-Loeve (K-L) expansion to 16 dimension feature space in order to reduce the dimension of the problem. Next we select two dimensional K-L features, which are outcomes of K-L expansion, through a calculation of the Euclidean distance measure. Finally we classify them based on two dimensional K-L features using the proposed neural network. The proposed TRBF neural network has three layers and a new learning algorithm. Its cost function for the learning process consists of a sum of squares error and a sum of inverse RBFs width. Through the learning process, the center and the width of each RBF and the weights between hidden and output layers are decided. The performances of the K-L features and the TRBF neural network are evaluated through the round-robin method in which one sample is tested after learning based on the rest of 127 samples. The two dimensional K-L features are more distinguishable than the raw two dimensional feature combination. The proposed TRBF network is able to define the trend of the distribution better than former RBF networks. According to the receiver operating characteristic (ROC) curve, this system indicates a better performance than one trained radiologist.

Proceedings Article | 16 April 1996 Paper

Detection of clustered microcalcifications using fuzzy modeling and convolution neural network

Jyh-Shyan Lin, Akira Hasegawa, Matthew Freedman, Huai Li, Shih-Chung Lo, Seong Mun, Osamu Tsujii

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237921

KEYWORDS: Fuzzy logic, Neural networks, Convolution, Mammography, Computing systems, Databases, Imaging systems, Image classification, Wavelets, Image filtering

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Proceedings Article | 15 April 1996 Paper

Optimization of orthonormal wavelet decomposition: implication of data accuracy, feature preservation, and compression effects

Yue Wang, Seong Mun, Huai Li, Matthew Freedman, Shih-Chung Lo

Proceedings Volume 2707, (1996) https://doi.org/10.1117/12.238448

KEYWORDS: Wavelets, Neural networks, Quantization, Image compression, Convolution, Mammography, Linear filtering, Filtering (signal processing), Signal to noise ratio, Wavelet transforms

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Proceedings Article | 11 April 1996 Paper

Dual-energy in mammography: feasibility study

Huai Li, Hamid Jafroudi, Seong Mun, Shih-Chung Lo, Matthew Freedman, Dorothy Steller Artz

Proceedings Volume 2708, (1996) https://doi.org/10.1117/12.237789

KEYWORDS: Mammography, X-rays, Imaging systems, Signal to noise ratio, Computing systems, Radiography, Sensors, Breast, Tissues, Signal attenuation

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Proceedings Article | 12 May 1995 Paper

Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiiographs

Matthew Freedman, Seong Mun, Shih-Chung Lo, Jyh-Shyan Lin

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208728

KEYWORDS: Neurons, Feature extraction, Computer aided diagnosis and therapy, Lung, Chest, Artificial neural networks, Chest imaging, Image processing, Algorithm development, Digital image processing

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Proceedings Article | 12 May 1995 Paper

Artificial convolution neural network with wavelet kernels for disease pattern recognition

Shih-Chung Lo, Chris Wu, Matthew Freedman, Seong Mun, Huai Li, Jyh-Shyan Lin, Akira Hasegawa

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208730

KEYWORDS: Wavelets, Convolution, Neural networks, Linear filtering, Pattern recognition, Wavelet transforms, Feature extraction, Mammography, Signal processing, Matrices

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Proceedings Article | 12 May 1995 Paper

Computerized detection and classification of microcalcifications on mammograms

Dorit Adler, Kwok Lam, Shih-Chung Lo, Datong Wei, Mark Helvie, Berkman Sahiner, Heang-Ping Chan

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208734

KEYWORDS: Mammography, Signal to noise ratio, Computer aided diagnosis and therapy, Image segmentation, Signal processing, Visibility, Shape analysis, Feature extraction, Algorithm development, Signal detection

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Proceedings Article | 8 May 1995 Paper

Performance characteristics of high-resolution charge-coupled device film digitizers

Philip Butson, Huai Li, Matthew Freedman, Seong Mun, Shih-Chung Lo, Jyh-Shyan Lin

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208367

KEYWORDS: Charge-coupled devices, Scanners, Absorbance, Modulation transfer functions, Mammography, Image resolution, Spatial resolution, Distortion, Systems modeling, Breast

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Proceedings Article | 8 May 1995 Paper

Digital mammography: the effects of decreased exposure

Shih-Chung Lo, Hamid Jafroudi, Jyh-Shyan Lin, Wendelin Hayes, Seong Mun, Dorothy Steller Artz, Rebecca Zuurbier, Raj Katial, Chris Wu, Matthew Freedman

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208369

KEYWORDS: Digital mammography, Chlorine, Image processing, Radiography, Mammography, Breast, Digital image processing, Imaging systems, Absorption, Calcium

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Proceedings Article | 8 May 1995 Paper

Digital mammography: tradeoffs between 50- and 100-micron pixel size

Chris Wu, Dorothy Steller Artz, Matthew Freedman, Hamid Jafroudi, Rebecca Zuurbier, Shih-Chung Lo, Walid Tohme, Jyh-Shyan Lin, Raj Katial, Richard Steinman, Wendelin Hayes, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208387

KEYWORDS: Digital mammography, Cancer, Breast, Sensors, Mammography, Breast cancer, Biopsy, Radiology, Data storage, Imaging systems

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Proceedings Article | 27 April 1995 Paper

Academic consortium for the evaluation of computer-aided diagnosis (CADx) in mammography

Datong Wei, Laurence Clarke, Nicholas Petrick, Berkman Sahiner, Bob Clark, Joseph Lo, Seong Mun, Shih-Chung Lo, Dev Chakraborty, Chris Wu, Maria Kallergi, Carey Floyd, Yongmin Kim, Matthew Freedman, Heang-Ping Chan, Mark Helvie

Proceedings Volume 2431, (1995) https://doi.org/10.1117/12.207638

KEYWORDS: Computer aided diagnosis and therapy, Mammography, Computing systems, Algorithm development, Breast cancer, Databases, Optimization (mathematics), Cancer, Digital mammography, Detection and tracking algorithms

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Proceedings Article | 27 April 1995 Paper

Full-frame compression of discrete wavelet and cosine transforms

Matthew Freedman, Brian Krasner, Shih-Chung Lo, Huai Li, Seong Mun

Proceedings Volume 2431, (1995) https://doi.org/10.1117/12.207613

KEYWORDS: Image compression, Transform theory, Mammography, Wavelets, Chest imaging, Quantization, Digital mammography, Computing systems, Medical imaging, Image resolution

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Proceedings Article | 11 May 1994 Paper

Comparison of conventional magnification mammography using film screen mammography and electronic magnification

Seong Mun, Matthew Freedman, Shih-Chung Lo, Francine Fields, Rebecca Zuurbier

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175104

KEYWORDS: Mammography, Image processing, Cancer, Breast, Breast cancer, Biopsy, Visibility, Image enhancement, Diagnostics, Visualization

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The detection of breast microcalcifications is an important finding that can indicate the presence of breast cancer. Once detected, the radiologist attempts to classify the microcalcifications into patterns that are associated with benign or malignant processes. If they cannot be so classified they are considered indeterminate calcifications. Those characteristics of the microcalcifications that are used in this important distinction are the shape and number of calcifications and the presence or absence of any change from a prior mammogram. (1-4) Geometric magnification views are often obtained to provide better visualization of the microcalcifications. Although conventional screen film mammography has sufficient high contrast resolution (20 line pairs per mm (lplmm)) to demonstrate these findings and the type of hand held magnifying lens often used by Radiologists has sufficient power to demonstrate more than 30 lp/mm, in practice, the region containing the microcalcifications is often in a low contrast region of the image and the hand held magnifying lens does not suffice. We produce electronic magnification views of the breast by taking an existing screen film mammogram and digitizing it with a small pixel size. (5) The image is then displayed on a monitor or printed on a laser printer that has a larger pixel size, thereby resulting in magnification. The visibility of the calcifications is enhanced by changing the window level and window width to increase optical density or to decrease luminance and to increase contrast. The effect of these two actions is to correct in part for the low contrast in the original image. Three potential benefits results from electronic magnification: 1. The current film on which calcifications are seen can be magnified and evaluated without the patient having to return for a geometric magnification view. 2. The prior mammogram (if of suboptimal quality) can be digitized and re-displayed to better determine whether the microcalcifications have changed. 3. The digitized image may allow the reclassification of calcifications from benign appearance to a pattern suggestive of cancer, thereby resulting in an earlier decision to biopsy.

Proceedings Article | 11 May 1994 Paper

Convolution neural-network-based detection of lung structures

Matthew Freedman, Shih-Chung Lo, Seong Mun, Akira Hasegawa

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175101

KEYWORDS: Lung, Neural networks, Chest imaging, Image processing, Convolution, Computer aided diagnosis and therapy, Heart, Evolutionary algorithms, Chest, Diagnostics

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Proceedings Article | 11 May 1994 Paper

Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer

Chris Wu, Akira Hasegawa, Matthew Freedman, Shih-Chung Lo, Seong Mun, Rebecca Zuurbier

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175099

KEYWORDS: Breast cancer, Neural networks, Radiography, Mammography, Image processing, Biopsy, Diagnostics, Breast, Databases, Convolution

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Proceedings Article | 11 May 1994 Paper

Narrow bandwidth spectral analysis of the textures of interstitial lung diseases

Shih-Chung Lo, Seong Mun, Brian Krasner

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175116

KEYWORDS: Opacity, Lung, Statistical analysis, Convolution, Image processing, Radiography, Neural networks, Image filtering, Diagnostics, Optical filters

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Proceedings Article | 11 May 1994 Paper

Image processing of storage phosphor musculoskeletal radiographs: a comparison of the AGFA and Fuji bone algorithms

Einar Pe, Martha Nelson, Shih-Chung Lo, Donald Smith, Seong Mun, Matthew Freedman, Robert Leckie

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175125

KEYWORDS: Image processing, Radon, Absorbance, Spatial frequencies, Radiography, Bone, Visibility, Image enhancement, Tissues, Imaging systems

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Proceedings Article | 1 May 1994 Paper

Image processing in digital mammography

Einar Pe, Shih-Chung Lo, Seong Mun, Matthew Freedman, Hamid Jafroudi, Martha Nelson, Rebecca Zuurbier, Raj Katial

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.174039

KEYWORDS: Image processing, Digital mammography, Mammography, Breast, Absorbance, Spatial frequencies, Digital image processing, Calcium, Optical spheres, Visibility

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Proceedings Article | 1 May 1994 Paper

Radiological image compression: image characteristics and clinical considerations

Brian Krasner, Seong Mun, Matthew Freedman, Man-Bae Kim, Huai Li, Shih-Chung Lo

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.174009

KEYWORDS: Image compression, Image quality, Image processing, Quantization, Medical imaging, Modulation transfer functions, Mammography, Image resolution, Image storage, Radiography

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Proceedings Article | 1 April 1994 Paper

Storage phosphor images in the neonatal intensive care unit: a multiobserver preference study

David Pearce, Matthew Freedman, Seong Mun, Einar Pe, Shih-Chung Lo, Clifton Leftridge, Dorothy Steller Artz, Martha Nelson

Proceedings Volume 2166, (1994) https://doi.org/10.1117/12.171732

KEYWORDS: Imaging systems, Absorbance, Image resolution, Radiography, Image quality, Collimation, Nonimpact printing, Chest imaging, Chest, Collimators

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Proceedings Article | 14 September 1993 Paper

Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network

Matthew Freedman, Shih-Chung Lo, Jyh-Shyan Lin, Seong Mun

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154572

KEYWORDS: Neural networks, Convolution, Lung, Image processing, Pattern recognition, Image enhancement, Diagnostics, Electroluminescence, Artificial neural networks, Chest imaging

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Proceedings Article | 30 June 1993 Paper

Gain of using irreversible over error-free data compression in digital radiography

Matthew Freedman, Seong Mun, Brian Krasner, Shih-Chung Lo

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.146978

KEYWORDS: Image compression, Radiography, Digital imaging, Medical imaging, Signal to noise ratio, Computed tomography, Image processing, Digital image processing, Data compression, Image quality

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Proceedings Article | 30 June 1993 Paper

Design of labels to be placed on film to be digitized or copied

Shih-Chung Lo, Martha Nelson, Matthew Freedman, Dennis Stewart, Louis Possilico

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.146996

KEYWORDS: Ultraviolet radiation, X-rays, Helium neon lasers, Laser scanners, Light sources, Radiology, Opacity, X-ray imaging, 3D scanning, Telecommunications

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Proceedings Article | 30 June 1993 Paper

Potential for unnecessary patient exposure from the use of storage phosphor imaging systems

Seong Mun, Shih-Chung Lo, Matthew Freedman, Martha Nelson, Einar Pe

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.146998

KEYWORDS: Imaging systems, Chest, Radiography, Absorbance, Signal to noise ratio, Control systems, Chest imaging, Automatic exposure, Calibration, Prototyping

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Proceedings Article | 30 June 1993 Paper

High-resolution (35 micron) CCD-based film digitizer for mammography

Shih-Chung Lo, Matthew Freedman, Seong Mun, Tomasa Dawkins

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.147001

KEYWORDS: Image processing, Mammography, Imaging systems, Digital image processing, Image resolution, Digital imaging, Image quality, Absorbance, Image enhancement, CCD cameras

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Proceedings Article | 30 June 1993 Paper

Image optimization procedures for the Fuji AC-1

Seong Mun, Matthew Freedman, Martha Nelson, Shih-Chung Lo, Einar Pe

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.146999

KEYWORDS: Absorbance, Radon, Image filtering, Image processing, Image enhancement, Bone, Lung, Spatial frequencies, Radiography, Optical filters

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Proceedings Article | 1 July 1992 Paper

Comparison of case retrieval times: film versus PACS

Harold Benson, Gregory Goger, Brian Garra, Brian Krasner, Shih-Chung Lo, Betty Levine, Seong Mun, Robert Fielding, Steven Horii

Proceedings Volume 1654, (1992) https://doi.org/10.1117/12.60275

KEYWORDS: Picture Archiving and Communication System, Image retrieval, Ultrasonography, Computed tomography, Reliability

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Proceedings Article | 1 June 1992 Paper

Computer-assisted diagnosis for lung nodule detection using a neural network technique

Shih-Chung Lo, Jyh-Shyan Lin, Matthew Freedman, Brian Krasner, Seong Mun

Proceedings Volume 1652, (1992) https://doi.org/10.1117/12.59479

KEYWORDS: Neural networks, Lung, Image processing, Image enhancement, Medical imaging, Picture Archiving and Communication System, Radiography, Chest, Databases, Radiology

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Proceedings Article | 1 May 1992 Paper

Test of radiologist performance in interpreting bedside chest examinations on a workstation

Kathleen Reagan, Steven Horii, Shih-Chung Lo, Matthew Freedman, Seong Mun, Martha Nelson

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59494

KEYWORDS: Diagnostics, Lung, Chest, Radiography, Interference (communication), Chest imaging, Nonimpact printing, Image quality, Image resolution, Picture Archiving and Communication System

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Proceedings Article | 1 May 1992 Paper

Hybrid coding for split gray values in radiological image compression

Seong Mun, Brian Krasner, Shih-Chung Lo, Steven Horii

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59529

KEYWORDS: Image compression, Quantization, Computer programming, Radiography, Picture Archiving and Communication System, Computed tomography, Image processing, Data storage, Medical imaging, Algorithm development

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Proceedings Article | 1 May 1992 Paper

Effect of vector quantization on ultrasound tissue characterization

Brian Krasner, Shih-Chung Lo, Seong Mun, Brian Garra

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59499

KEYWORDS: Tissues, Quantization, Ultrasonography, Image compression, Distortion, Liver, Feature extraction, Fractal analysis, Error analysis

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Proceedings Article | 1 July 1991 Paper

PACS reading time comparision: the workstation versus alternator for ultrasound

Shih-Chung Lo, Robert Zeman, Steven Horii, Robert Fielding, Betty Levine, Jon Singer, Seong Mun, Brian Garra

Proceedings Volume 1446, (1991) https://doi.org/10.1117/12.45310

KEYWORDS: Picture Archiving and Communication System, Ultrasonography, Image resolution, Medical imaging, Surgery, Magnetic resonance imaging, Time metrology, Radiology, Chest imaging, Tomography

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Proceedings Article | 1 May 1991 Paper

Full-frame entropy coding for radiological image compression

Seong Mun, Shih-Chung Lo, Brian Krasner, Steven Horii

Proceedings Volume 1444, (1991) https://doi.org/10.1117/12.45178

KEYWORDS: Image compression, Quantization, Computed tomography, Digital imaging, Image processing, Chest, Chromium, Computer programming, Error analysis, Matrices

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Proceedings Article | 1 July 1990 Paper

Image splitting and remapping method for radiological image compression

Ellen Shen, Shih-Chung Lo, Seong Mun

Proceedings Volume 1232, (1990) https://doi.org/10.1117/12.18870

KEYWORDS: Image compression, Medical imaging, Computer programming, Distortion, Computed tomography, Quantization, Image quality, Chest, Reconstruction algorithms, Chest imaging

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