Dr. Andrew F. Laine Profile

Dr. Andrew F. Laine

Associate Professor at Columbia Univ

SPIE Involvement:

Author

Publications (39)

Proceedings Article | 2 April 2024 Presentation + Paper

Label refinement for noisy annotation in weakly supervised segmentation

Ziyi Huang, Hongshan Liu, Haofeng Zhang, Fuyong Xing, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan

Proceedings Volume 12926, 129260W (2024) https://doi.org/10.1117/12.3006817

KEYWORDS: Image segmentation, Neural networks, Optical coherence tomography, Medical imaging, Deep learning

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Proceedings Article | 5 March 2021 Presentation

Automated adipose segmentation within cardiac optical coherence tomography images

Ziyi Huang, Yu Gan, Theresa Lye, Yanchen Liu, Andrew Laine, Elsa Angelini, Christine Hendon

Proceedings Volume 11621, 1162107 (2021) https://doi.org/10.1117/12.2578814

KEYWORDS: Image segmentation, Optical coherence tomography, Heart, Tissues, Spectral resolution, Radiofrequency ablation, Machine learning, Image resolution, Image processing algorithms and systems, Binary data

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SPIE Journal Paper | 12 July 2016

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

Auranuch Lorsakul, Georges El Fakhri, William Worstell, Jinsong Ouyang, Yothin Rakvongthai, Andrew Laine, Quanzheng Li

JMI, Vol. 3, Issue 03, 035501, (July 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.3.035501

KEYWORDS: Computed tomography, Signal attenuation, Signal to noise ratio, Sensors, Image filtering, X-ray computed tomography, Arteries, Imaging systems, X-rays, Medical imaging

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Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre–Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p<0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p<0.01) for FBP-Ramp images and 53.2% to 67.7% (all p<0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.

SPIE Journal Paper | 1 October 2011 Open Access

Recovery of macular pigment spectrum in vivo using hyperspectral image analysis

Amani Fawzi, Noah Lee, Andrew Laine, Jennifer Acton, R. Theodore Smith

JBO, Vol. 16, Issue 10, 106008, (October 2011) https://doi.org/10.1117/12.10.1117/1.3640813

KEYWORDS: Absorbance, In vivo imaging, Reflectivity, Hyperspectral imaging, Image analysis, In vitro testing, Atrial fibrillation, Cameras, Eye, Data modeling

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Proceedings Article | 4 March 2009 Paper

Interactive segmentation in multi-modal brain imagery using a Bayesian transductive learning approach

Noah Lee, Jesus Caban, Shahram Ebadollahi, Andrew Laine

Proceedings Volume 7260, 72601W (2009) https://doi.org/10.1117/12.811675

KEYWORDS: Image segmentation, Data modeling, Medical imaging, Brain, Solid state lighting, Neuroimaging, Statistical modeling, Image processing algorithms and systems, Image classification, Tumors

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Proceedings Article | 18 March 2008 Paper

Automatic detection of blood versus non-blood regions on intravascular ultrasound (IVUS) images using wavelet packet signatures

Amin Katouzian, Babak Baseri, Elisa Konofagou, Andrew Laine

Proceedings Volume 6920, 69200H (2008) https://doi.org/10.1117/12.773600

KEYWORDS: Intravascular ultrasound, Wavelets, Blood, Tissues, Transducers, Image segmentation, In vivo imaging, In vitro testing, Arteries, Feature extraction

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Proceedings Article | 17 March 2008 Paper

True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Noah Lee, Huseyin Tek, Andrew Laine

Proceedings Volume 6915, 69152V (2008) https://doi.org/10.1117/12.770610

KEYWORDS: Wavelets, Image segmentation, Data modeling, Solid modeling, Medical imaging, Wavelet transforms, Visual process modeling, Visualization, Computer aided diagnosis and therapy, Diagnostics

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Proceedings Article | 15 March 2008 Paper

Faster, higher quality volume visualization for 3D medical imaging

Alan Kalvin, Andrew Laine, Ting Song

Proceedings Volume 6918, 691830 (2008) https://doi.org/10.1117/12.770990

KEYWORDS: Visualization, 3D image processing, Volume visualization, Data acquisition, Biomedical optics, Image processing, Sensors, 3D image reconstruction, Image quality, X-rays

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Proceedings Article | 22 March 2007 Paper

Augmenting CT cardiac roadmaps with segmented streaming ultrasound

Qi Duan, Guy Shechter, Luis Gutiérrez, Douglas Stanton, Lyubomir Zagorchev, Andrew Laine, Daniel Elgort

Proceedings Volume 6509, 65090V (2007) https://doi.org/10.1117/12.711431

KEYWORDS: Image segmentation, Ultrasonography, Image registration, X-ray computed tomography, 3D modeling, Finite element methods, 3D image processing, Computed tomography, Image resolution, Motion models

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Proceedings Article | 15 March 2006 Paper

Variational segmentation framework in prolate spheroidal coordinates for 3D real-time echocardiography

Bruno Vallet, Elsa Angelini, Andrew Laine

Proceedings Volume 6144, 61444A (2006) https://doi.org/10.1117/12.653692

KEYWORDS: Data modeling, 3D modeling, Image segmentation, Motion models, Echocardiography, Ultrasonography, Heart, 3D image processing, Spherical lenses, 3D scanning

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Proceedings Article | 29 April 2005 Paper

Recognition of micro-array protein crystals images using multi-scale representations

Ya Wang, David Kim, Elsa Angelini, Andrew Laine

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595902

KEYWORDS: Crystals, Proteins, Image classification, Image processing, Radon transform, Image filtering, Hough transforms, Neural networks, Databases, Feature extraction

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Proceedings Article | 12 April 2005 Paper

Evaluation of optical flow algorithms for tracking endocardial surfaces on three-dimensional ultrasound data

Qi Duan, Elsa Angelini, Susan Herz, Christopher Ingrassia, Olivier Gerard, Kevin Costa, Jeffrey Holmes, Andrew Laine

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.596890

KEYWORDS: Optical flow, Ultrasonography, Optical tracking, Detection and tracking algorithms, Data acquisition, 3D image processing, Transducers, Visualization, 3D acquisition, Automatic tracking

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With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. A finite element surface was fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency between manual tracing and optical flow estimation on 73% of the entire surface with fewer than 10% difference. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets.

Proceedings Article | 12 April 2005 Paper

Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography

Gert Schoonenberg, Marc Schrijver, Qi Duan, Richard Kemkers, Andrew Laine

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.596535

KEYWORDS: Image filtering, Digital filtering, Spatial filters, X-rays, Fluoroscopy, X-ray imaging, Angiography, Image processing, Detection and tracking algorithms, Electronic filtering

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Proceedings Article | 12 May 2004 Paper

Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model

Elsa Angelini, Ting Song, Brett Mensh, Andrew Laine

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535860

KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, Tissues, 3D modeling, 3D image processing, Data modeling, Binary data, Neuroimaging, Clinical research

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Proceedings Article | 28 April 2004 Paper

Assessment of visual quality and spatial accuracy of fast anisotropic diffusion and scan conversion algorithms for real-time three-dimensional spherical ultrasound

Qi Duan, Elsa Angelini, Andrew Laine

Proceedings Volume 5373, (2004) https://doi.org/10.1117/12.535813

KEYWORDS: Anisotropic diffusion, Ultrasonography, Spherical lenses, Denoising, Data conversion, Image quality, Anisotropic filtering, Speckle, Diffusion, Visualization

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Proceedings Article | 9 May 2002 Paper

Improving statistics for hybrid segmentation of high-resolution multichannel images

Elsa Angelini-Casadevall, Celina Imielinska, Yinpeng Jin, Andrew Laine

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467182

KEYWORDS: Image segmentation, Quantization, RGB color model, Magnetic resonance imaging, Fuzzy logic, Binary data, Tissues, Brain, Bone, Distance measurement

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Proceedings Article | 9 May 2002 Paper

Adaptive speed term based on homogeneity for level-set segmentation

Yinpeng Jin, Andrew Laine, Celina Imielinska

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467180

KEYWORDS: Image segmentation, Medical imaging, Berkelium, Image processing, Visual process modeling, Machine vision, Computer vision technology, Visualization, Tolerancing, 3D acquisition

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Proceedings Article | 5 December 2001 Paper

Contrast enhancement by multiscale adaptive histogram equalization

Yinpeng Jin, Laura Fayad, Andrew Laine

Proceedings Volume 4478, (2001) https://doi.org/10.1117/12.449705

KEYWORDS: Chest, Image enhancement, Wavelets, Lung, Tissues, X-ray computed tomography, Computed tomography, Image contrast enhancement, Bone, Diagnostics

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Proceedings Article | 5 December 2001 Paper

Regularization in tomographic reconstruction using thresholding estimators

Jerome Kalifa, Andrew Laine, Peter Esser

Proceedings Volume 4478, (2001) https://doi.org/10.1117/12.449736

KEYWORDS: Wavelets, Tomography, Reconstruction algorithms, Radon transform, Error analysis, Wavelet transforms, Single photon emission computed tomography, Positron emission tomography, Image restoration, Medical imaging

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Proceedings Article | 4 December 2000 Paper

Tomographic reconstruction with nonlinear diagonal estimators

Jerome Kalifa, Andrew Laine, Peter Esser

Proceedings Volume 4119, (2000) https://doi.org/10.1117/12.408646

KEYWORDS: Wavelets, Tomography, Radon transform, Reconstruction algorithms, Wavelet transforms, Radon, Expectation maximization algorithms, Positron emission tomography, Error analysis, Signal to noise ratio

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Proceedings Article | 4 December 2000 Paper

Spatiotemporal directional analysis of 4D echocardiography

Elsa Angelini-Casadevall, Andrew Laine, Shin Takuma, Shunichi Homma

Proceedings Volume 4119, (2000) https://doi.org/10.1117/12.408649

KEYWORDS: Speckle, Interference (communication), Ultrasonography, Denoising, Fourier transforms, Signal to noise ratio, Echocardiography, Spatial resolution, Phase measurement, Data modeling

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Proceedings Article | 4 December 2000 Paper

Evaluation of a multiscale enhancement protocol for digital mammography

Ralf Mekle, Andrew Laine, Suzanne Smith, Cory Singer, Tova Koenigsberg, Marc Brown

Proceedings Volume 4119, (2000) https://doi.org/10.1117/12.408591

KEYWORDS: Mammography, Wavelets, Diagnostics, Digital mammography, Statistical analysis, Lab on a chip, Databases, Electronic filtering, Error analysis, Human-machine interfaces

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Proceedings Article | 4 December 2000 Paper

Activation detection in fMRI data via multiscale singularity detection

Ralf Mekle, Andrew Laine, Gerard Perera, Robert DeLaPaz

Proceedings Volume 4119, (2000) https://doi.org/10.1117/12.408651

KEYWORDS: Wavelets, Signal detection, Interference (communication), Functional magnetic resonance imaging, Wavelet transforms, Data modeling, Brain, Brain mapping, Signal to noise ratio, Lithium

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Proceedings Article | 22 March 1999 Paper

Multiresolution stereo algorithm via wavelet representations for autonomous navigation

Minbo Shim, John Kurtz, Andrew Laine

Proceedings Volume 3723, (1999) https://doi.org/10.1117/12.342941

KEYWORDS: Wavelets, Wavelet transforms, Unmanned ground vehicles, Extreme ultraviolet, Cameras, Detection and tracking algorithms, Reliability, Image resolution, Navigation systems, Visualization

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Proceedings Article | 26 March 1998 Paper

Wavelet CFAR detector for mass detection in mammography

Dongwei Chen, John Harris, Andrew Laine, Walter Huda

Proceedings Volume 3391, (1998) https://doi.org/10.1117/12.304916

KEYWORDS: Wavelets, Sensors, Mammography, Detection and tracking algorithms, Digital mammography, Signal detection, Breast cancer, Tumors, Wavelet transforms, Detection theory

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Proceedings Article | 23 October 1996 Paper

Multiscale suboctave wavelet transform for denoising and enhancement

Andrew Laine, Xuli Zong

Proceedings Volume 2825, (1996) https://doi.org/10.1117/12.255235

KEYWORDS: Wavelets, Wavelet transforms, Stationary wavelet transform, Discrete wavelet transforms, Image enhancement, Denoising, Finite impulse response filters, Mammography, Interference (communication), Signal processing

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Proceedings Article | 16 April 1996 Paper

Homomorphic wavelet shrinkage and feature emphasis for speckle reduction and enhancement of echocardiographic images

Xuli Zong, Edward Geiser, Andrew Laine, David Wilson

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237969

KEYWORDS: Wavelets, Speckle, Discrete wavelet transforms, Digital filtering, Electronic filtering, Image enhancement, Ultrasonography, Wavelet transforms, Denoising, Interference (communication)

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Proceedings Article | 22 March 1996 Paper

De-noising and contrast enhancement via wavelet shrinkage and nonlinear adaptive gain

Xuli Zong, Andrew Laine, Edward Geiser, David Wilson

Proceedings Volume 2762, (1996) https://doi.org/10.1117/12.236028

KEYWORDS: Wavelets, Discrete wavelet transforms, Interference (communication), Wavelet transforms, Signal processing, Feature extraction, Image enhancement, Denoising, Diffusion, Smoothing

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Proceedings Article | 27 February 1996 Paper

Automatic scale detection

Dongwei Chen, John Harris, Andrew Laine

Proceedings Volume 2727, (1996) https://doi.org/10.1117/12.233313

KEYWORDS: Wavelets, Image enhancement, Analog electronics, Wavelet transforms, Smoothing, Resistors, Image processing, Mammography, Signal detection, Image filtering

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Proceedings Article | 12 May 1995 Paper

Comparison of a dyadic wavelet image enhancement algorithm with unsharp masking and median filtering for mammography

Yunong Xing, Walter Huda, Andrew Laine, Jian Fan, Barbara Steinbach, Janice Honeyman-Buck

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208745

KEYWORDS: Image processing, Signal to noise ratio, Wavelets, Digital filtering, Image enhancement, Mammography, Interference (communication), Image filtering, Computer simulations, Signal processing

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Proceedings Article | 6 April 1995 Paper

Steerable dyadic wavelet transform and interval wavelets for enhancement of digital mammography

Andrew Laine, Iztok Koren, Wuhai Yang, Fred Taylor

Proceedings Volume 2491, (1995) https://doi.org/10.1117/12.205437

KEYWORDS: Mammography, Image enhancement, Wavelet transforms, Wavelets, Digital mammography, Nonlinear filtering, Image processing, Image filtering, Breast, Optical filters

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Proceedings Article | 11 October 1994 Paper

Discrete dyadic wavelets for contrast enhancement

Andrew Laine, Jian Fan, Sergio Schuler

Proceedings Volume 2303, (1994) https://doi.org/10.1117/12.188796

KEYWORDS: Wavelets, Image enhancement, Wavelet transforms, Radon, Medical imaging, Enhanced vision, Machine vision, Information visualization, Algorithms, Gaussian filters

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Proceedings Article | 11 October 1994 Paper

Quantitative evaluation of wavelet-based image processing algorithms

Zhenxue Jing, Yisheng Zheng, Walter Huda, Andrew Laine, Jian Fan, Yunong Xing

Proceedings Volume 2303, (1994) https://doi.org/10.1117/12.188807

KEYWORDS: Image processing, Signal to noise ratio, Wavelets, Interference (communication), Image enhancement, Algorithm development, Mammography, Signal processing, Mathematical modeling, Visibility

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Proceedings Article | 8 July 1994 Paper

Simulated phantom images for optimizing wavelet-based image processing algorithms in mammography

Yunong Xing, Walter Huda, Andrew Laine, Jian Fan

Proceedings Volume 2299, (1994) https://doi.org/10.1117/12.179251

KEYWORDS: Image processing, Computer simulations, Wavelets, Mammography, Interference (communication), Signal detection, Image enhancement, Signal processing, Wavelet transforms, Algorithm development

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Proceedings Article | 1 November 1993 Paper

Adaptive approach for texture segmentation by multichannel wavelet frames

Andrew Laine, Jian Fan

Proceedings Volume 2034, (1993) https://doi.org/10.1117/12.162071

KEYWORDS: Image segmentation, Wavelets, Feature extraction, Digital filtering, Nonlinear filtering, Edge detection, Image filtering, Discrete wavelet transforms, Detection and tracking algorithms, Signal analyzers

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Proceedings Article | 14 September 1993 Paper

Hexagonal wavelet processing of digital mammography

Andrew Laine, Sergio Schuler, Walter Huda, Janice Honeyman-Buck, Barbara Steinbach

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154543

KEYWORDS: Mammography, Image enhancement, Image processing, Image filtering, Wavelets, Digital mammography, Fourier transforms, Breast, Wavelet transforms, Multiscale representation

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Proceedings Article | 29 July 1993 Paper

Adaptive multiscale processing for contrast enhancement

Andrew Laine, Shuwu Song, Jian Fan, Walter Huda, Janice Honeyman, Barbara Steinbach

Proceedings Volume 1905, (1993) https://doi.org/10.1117/12.148666

KEYWORDS: Mammography, Wavelet transforms, Wavelets, Breast, Digital mammography, Image enhancement, Visualization, Tissues, Multiscale representation, Pathology

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Proceedings Article | 9 December 1992 Paper

Multiscale wavelet representations for mammographic feature analysis

Andrew Laine, Shuwu Song

Proceedings Volume 1768, (1992) https://doi.org/10.1117/12.130912

KEYWORDS: Wavelets, Wavelet transforms, Mammography, Transform theory, Image enhancement, Breast, Medical imaging, Biological research, Signal detection, Visualization

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Proceedings Article | 22 September 1992 Paper

Wavelet processing techniques for digital mammography

Andrew Laine, Shuwu Song

Proceedings Volume 1808, (1992) https://doi.org/10.1117/12.131114

KEYWORDS: Wavelets, Wavelet transforms, Mammography, Visualization, Biomedical optics, Image enhancement, Breast, Digital mammography, Biological research, Breast cancer

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