Prof. David R. Haynor Profile

Prof. David R. Haynor

Professor at Univ of Washington

SPIE Involvement:

Conference Program Committee | Conference Chair | Author | Editor

Publications (52)

PROCEEDINGS ARTICLE | March 12, 2018

Automated connectivity-based cortical mapping using registration-constrained classification

Proc. SPIE. 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Data modeling, Matrices, Diffusion, Data acquisition, Neural networks, Neuroimaging, Brain mapping, Functional magnetic resonance imaging, Brain

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PROCEEDINGS ARTICLE | February 27, 2018

Deep-learning derived features for lung nodule classification with limited datasets

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Convolutional neural networks, Image processing, Diagnostics, Feature extraction, Lung, Machine learning, Computed tomography, Image classification, Lung cancer, Network architectures

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SPIE Conference Volume | April 11, 2013

Medical Imaging 2013: Image Processing

PROCEEDINGS ARTICLE | March 13, 2013

A flexible toolkit for rapid GPU-based generation of DRRs for 2D-3D registration

Proc. SPIE. 8669, Medical Imaging 2013: Image Processing

KEYWORDS: Data storage, Image segmentation, Composites, Kinematics, Image registration, Bone, Image intensifiers, Data centers, Gait analysis, 3D image processing

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SPIE Conference Volume | March 30, 2012

Medical Imaging 2012: Image Processing

PROCEEDINGS ARTICLE | February 16, 2012

Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images

Proc. SPIE. 8319, Medical Imaging 2012: Advanced PACS-based Imaging Informatics and Therapeutic Applications

KEYWORDS: Liver, Detection and tracking algorithms, Data modeling, Databases, Heart, Feature extraction, Image registration, Abdomen, Kidney, Computed tomography

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PROCEEDINGS ARTICLE | March 12, 2011

Robust linear registration of CT images using random regression forests

Proc. SPIE. 7962, Medical Imaging 2011: Image Processing

KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Databases, Image segmentation, Image registration, Medical imaging, Computed tomography, Optimization (mathematics), Stochastic processes, 3D image processing

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SPIE Conference Volume | March 7, 2011

Medical Imaging 2011: Image Processing

SPIE Conference Volume | March 5, 2010

Medical Imaging 2010: Image Processing

PROCEEDINGS ARTICLE | March 13, 2009

Validation of CT-video registration for guiding a novel ultrathin bronchoscope to peripheral lung nodules using electromagnetic tracking

Proc. SPIE. 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling

KEYWORDS: Actuators, Visual process modeling, Visualization, Image registration, Lung, Medical imaging, Printed circuit board testing, Electromagnetism, Current controlled current source, Thermal weapon sites

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PROCEEDINGS ARTICLE | February 28, 2009

Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

Proc. SPIE. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Genetic algorithms, Thalamus, Computer simulations, Spatial resolution, Neuroimaging, Algorithm development, Magnetorheological finishing, 3D image processing, Somatosensory cortex, Brain

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PROCEEDINGS ARTICLE | March 17, 2008

CT and MR image fusion for CSF leak diagnosis

Proc. SPIE. 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling

KEYWORDS: Image fusion, Visualization, Tissues, Magnetic resonance imaging, Image processing, Image registration, Bone, Medical imaging, Computed tomography, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | March 29, 2007

A model of respiratory airway motion for real-time tracking of an ultrathin bronchoscope

Proc. SPIE. 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images

KEYWORDS: Mathematical modeling, Chest, Data modeling, Image segmentation, Image resolution, Image registration, Lung, Computed tomography, Motion models, Bronchoscopy

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PROCEEDINGS ARTICLE | March 8, 2007

WHIPPET: a collaborative software environment for medical image processing and analysis

Proc. SPIE. 6512, Medical Imaging 2007: Image Processing

KEYWORDS: Magnetic resonance imaging, Image segmentation, Image processing, Image analysis, Medical imaging, Operating systems, 3D image processing, Functional magnetic resonance imaging, Diffusion tensor imaging, Brain

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PROCEEDINGS ARTICLE | March 10, 2006

Image segmentation and registration for the analysis of joint motion from 3D MRI

Proc. SPIE. 6141, Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display

KEYWORDS: Visualization, Magnetic resonance imaging, Image segmentation, Kinematics, Image registration, Bone, Motion analysis, 3D image processing, Polonium, 3D magnetic resonance imaging

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PROCEEDINGS ARTICLE | April 29, 2005

Prostate ultrasound image segmentation using level set-based region flow with shape guidance

Proc. SPIE. 5747, Medical Imaging 2005: Image Processing

KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Data modeling, Ultrasonography, Image segmentation, Image processing, Medical imaging, Prostate, Statistical modeling, Binary data

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PROCEEDINGS ARTICLE | April 29, 2005

Gene expression detection and expression visualization in in situ hybridized cross-sectional images of the mouse brain

Proc. SPIE. 5747, Medical Imaging 2005: Image Processing

KEYWORDS: Signal to noise ratio, Genetic algorithms, Detection and tracking algorithms, Visualization, Tissues, Image segmentation, Neuroimaging, Algorithm development, RGB color model, Brain

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PROCEEDINGS ARTICLE | April 12, 2005

MR and CT image fusion of the cervical spine: a noninvasive alternative to CT-myelography

Proc. SPIE. 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display

KEYWORDS: Image fusion, Tissues, Surgery, Magnetic resonance imaging, Image segmentation, Pathology, Image registration, Bone, Computed tomography, Spine

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PROCEEDINGS ARTICLE | May 12, 2004

Multirigid registration of MR and CT images of the cervical spine

Proc. SPIE. 5370, Medical Imaging 2004: Image Processing

KEYWORDS: Image fusion, Tissues, Surgery, Magnetic resonance imaging, Image segmentation, Image processing, Image registration, Bone, Computed tomography, Spine

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PROCEEDINGS ARTICLE | May 30, 2003

Prostate segmentation in ultrasound images with deformable shape priors

Proc. SPIE. 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display

KEYWORDS: Image processing algorithms and systems, Edge detection, Detection and tracking algorithms, Data modeling, Speckle, Ultrasonography, Image segmentation, 3D modeling, Prostate, Algorithm development

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PROCEEDINGS ARTICLE | May 17, 2002

Registration of prostate brachytherapy seeds with prostate anatomy for improved patient dosimetry

Proc. SPIE. 4681, Medical Imaging 2002: Visualization, Image-Guided Procedures, and Display

KEYWORDS: Tumors, Imaging systems, Ultrasonography, Image segmentation, Image registration, Prostate, Computed tomography, Prostate cancer, Fluoroscopy, 3D image processing

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PROCEEDINGS ARTICLE | April 11, 2002

Intensity-based image registration for 3D spatial compounding using a freehand 3D ultrasound system

Proc. SPIE. 4687, Medical Imaging 2002: Ultrasonic Imaging and Signal Processing

KEYWORDS: 3D acquisition, 3D image reconstruction, Sensors, Ultrasonography, Chromium, Image registration, Data acquisition, Image quality, In vivo imaging, 3D image processing

Read Abstract

3D spatial compounding involves the combination of two or more 3D ultrasound (US) data sets, acquired under different insonation angles and windows, to form a higher quality 3D US data set. An important requirement for this method to succeed is the accurate registration between the US images used to form the final compounded image. We have developed a new automatic method for rigid and deformable registration of 3D US data sets, acquired using a freehand 3D US system. Deformation is provided by using a 3D thin-plate spline (TPS). Our method is fundamentally different from the previous ones in that the acquired scattered US 2D slices are registered and compounded directly into the 3D US volume. Our approach has several benefits over the traditional registration and spatial compounding methods: (i) we only peform one 3D US reconstruction, for the first acquired data set, therefore we save the computation time required to reconstruct subsequent acquired scans, (ii) for our registration we use (except for the first scan) the acquired high-resolution 2D US images rather than the 3D US reconstruction data which are of lower quality due to the interpolation and potential subsampling associated with 3D reconstruction, and (iii) the scans performed after the first one are not required to follow the typical 3D US scanning protocol, where a large number of dense slices have to be acquired; slices can be acquired in any fashion in areas where compounding is desired. We show that by taking advantage of the similar information contained in adjacent acquired 2D US slices, we can reduce the computation time of linear and nonlinear registrations by a factor of more than 7:1, without compromising registration accuracy. Furthermore, we implemented an adaptive approximation to the 3D TPS with local bilinear transformations allowing additional reduction of the nonlinear registration computation time by a factor of approximately 3.5. Our results are based on a commercially available tissue-mimicking abdominal phantom and in-vivo muscle data.

PROCEEDINGS ARTICLE | July 3, 2001

Nonrigid multimodality image registration

Proc. SPIE. 4322, Medical Imaging 2001: Image Processing

KEYWORDS: Chest, Visualization, Image processing, Error analysis, Image resolution, Image registration, Lung, Abdomen, Computed tomography, Positron emission tomography

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PROCEEDINGS ARTICLE | July 3, 2001

Validation of brain segmentation and tissue classification algorithm for T1-weighted MR images

Proc. SPIE. 4322, Medical Imaging 2001: Image Processing

KEYWORDS: Image processing algorithms and systems, Tissues, Magnetic resonance imaging, Image segmentation, Image registration, Head, Image classification, Neuroimaging, Algorithm development, Brain

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PROCEEDINGS ARTICLE | June 6, 2000

General-purpose software tool for serial segmentation of stacked images

Proc. SPIE. 3979, Medical Imaging 2000: Image Processing

KEYWORDS: Image processing algorithms and systems, Human-machine interfaces, Detection and tracking algorithms, Image segmentation, Medical imaging, Image filtering, Algorithm development, Motion estimation, 3D image processing, Brain

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PROCEEDINGS ARTICLE | April 18, 2000

Image-based registration of ultrasound and magnetic resonance images: a preliminary study

Proc. SPIE. 3976, Medical Imaging 2000: Image Display and Visualization

KEYWORDS: Transmitters, Detection and tracking algorithms, Visualization, Surgery, Ultrasonography, Magnetic resonance imaging, Magnetism, Image registration, Medical imaging, Position sensors

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A number of surgical procedures are planned and executed based on medical images. Typically, x-ray computed tomography (CT) and magnetic resonance (MR) images are acquired preoperatively for diagnosis and surgical planning. In the operating room, execution of the surgical plan becomes feasible due to registration between preoperative images and surgical space where patient anatomy lies. In this paper, we present a new automatic algorithm where we use ultrasound (US) 2D B-mode images to register the preoperative MR image coordinate system with the surgical space which in our experiments is represented by the reference coordinate system of a DC magnetic position sensor. The position sensor is also used for tracking the position and orientation of the US images. Furthermore, we simulated patient anatomy by using custom-built phantoms. Our registration algorithm is a hybrid between fiducial- based and image-based registration algorithms. Initially, we perform a fiducial-based rigid-body registration between MR and position sensor space. Then, by changing various parameters of the rigid-body fiducial-based transformation, we produce an MR-sensor misregistration in order to simulate potential movements of the skin fiducials and/or the organs. The perturbed transformation serves as the initial estimate for the image-based registration algorithm, which uses normalized mutual information as a similarity measure, where one or more US images of the phantom are automatically matched with the MR image data set. By using the fiducial- based registration as the gold standard, we could compute the accuracy of the image-based registration algorithm in registering MR and sensor spaces. The registration error varied depending on the number of 2D US images used for registration. A good compromise between accuracy and computation time was the use of 3 US slices. In this case, the registration error had a mean value of 1.88 mm and standard deviation of 0.42 mm, whereas the required computation time was approximately 52 sec. Subsampling the US data by a factor of 4 X 4 and reducing the number of histogram bins to 128 reduced the computation time to approximately 6 sec. with a small increase in the registration error.

PROCEEDINGS ARTICLE | May 26, 1999

PC-based system for 3D registration of ultrasound and magnetic resonance images based on a magetic position sensor

Proc. SPIE. 3658, Medical Imaging 1999: Image Display

KEYWORDS: Transmitters, Ultrasonography, Magnetic resonance imaging, Magnetism, Receivers, Image registration, Transducers, Position sensors, 3D image processing, Brain

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PROCEEDINGS ARTICLE | June 26, 1998

Calibration and validation of freehand 3D ultrasound systems based on DC magnetic tracking

Proc. SPIE. 3335, Medical Imaging 1998: Image Display

KEYWORDS: Transmitters, Sensors, Calibration, Ultrasonography, Magnetism, Receivers, Position sensors, 3D image processing, Magnetic tracking, Tin

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PROCEEDINGS ARTICLE | June 26, 1998

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

Proc. SPIE. 3335, Medical Imaging 1998: Image Display

KEYWORDS: Image processing algorithms and systems, Statistical analysis, Speckle, Ultrasonography, Image segmentation, Image processing, Error analysis, Prostate, Prostate cancer, 3D image processing

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PROCEEDINGS ARTICLE | June 24, 1998

Unified data structures in a software environment for medical image segmentation

Proc. SPIE. 3338, Medical Imaging 1998: Image Processing

KEYWORDS: Image processing algorithms and systems, Tissues, Databases, Image segmentation, 3D modeling, Medical imaging, Software development, Image classification, Algorithm development, Computer architecture

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PROCEEDINGS ARTICLE | May 7, 1997

MedPlus: a medical image analysis and browsing environment

Proc. SPIE. 3031, Medical Imaging 1997: Image Display

KEYWORDS: Image visualization, Statistical analysis, Visualization, Databases, Image processing, Image analysis, Medical imaging, Head, Analytical research, Algorithm development

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PROCEEDINGS ARTICLE | April 25, 1997

Parameter estimation in deformable models using Markov chain Monte Carlo

Proc. SPIE. 3034, Medical Imaging 1997: Image Processing

KEYWORDS: Mathematical modeling, Statistical analysis, Data modeling, Image segmentation, Medical imaging, Monte Carlo methods, Stochastic processes, Statistical modeling, Expectation maximization algorithms, Data analysis

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PROCEEDINGS ARTICLE | April 25, 1997

Bayes and medical imaging: it's time to make priors a priority

Proc. SPIE. 3034, Medical Imaging 1997: Image Processing

KEYWORDS: Data modeling, Radiography, Image analysis, Medical imaging, Gaussian filters, Data processing, Image filtering, Information technology, Computed tomography, RGB color model

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PROCEEDINGS ARTICLE | April 16, 1996

Rapid coarse-to-fine matching using scale-specific priors

Proc. SPIE. 2710, Medical Imaging 1996: Image Processing

KEYWORDS: Infrared imaging, Thalamus, Data modeling, Magnetic resonance imaging, Image segmentation, Head, Information technology, Neuroimaging, Chemical elements, Brain

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PROCEEDINGS ARTICLE | May 12, 1995

Bayesian approach to the brain image matching problem

Proc. SPIE. 2434, Medical Imaging 1995: Image Processing

KEYWORDS: Infrared imaging, Data modeling, Tissues, Error analysis, Information technology, Associative arrays, Image classification, Neuroimaging, Brain mapping, Brain

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PROCEEDINGS ARTICLE | May 8, 1995

Quality assurance for CT and MRI scanners

Proc. SPIE. 2432, Medical Imaging 1995: Physics of Medical Imaging

KEYWORDS: Signal to noise ratio, X-ray computed tomography, Calibration, Magnetic resonance imaging, Scanners, Computing systems, Image resolution, Image analysis, Computed tomography, Radiology

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PROCEEDINGS ARTICLE | September 9, 1994

Matching structural images of the human brain using statistical and geometrical image features

Proc. SPIE. 2359, Visualization in Biomedical Computing 1994

KEYWORDS: Image fusion, Tissues, Image resolution, Image registration, Image filtering, Information technology, Image classification, Neuroimaging, Chemical elements, Brain

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PROCEEDINGS ARTICLE | May 11, 1994

Finite element approach to warping of brain images

Proc. SPIE. 2167, Medical Imaging 1994: Image Processing

KEYWORDS: Magnetic resonance imaging, Image processing, Finite element methods, Information technology, Neuroimaging, Chemical elements, Probability theory, 3D image processing, Brain mapping, Brain

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PROCEEDINGS ARTICLE | May 11, 1994

Left-ventricular boundary detection from short-axis echocardiograms: the use of active contour models

Proc. SPIE. 2167, Medical Imaging 1994: Image Processing

KEYWORDS: Edge detection, Detection and tracking algorithms, Ultrasonography, Image segmentation, Image processing, Digital filtering, Heart, 3D modeling, Image filtering, 3D image processing

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PROCEEDINGS ARTICLE | April 1, 1994

Design of a receiver operating characteristic (ROC) study of 10:1 lossy image compression

Proc. SPIE. 2166, Medical Imaging 1994: Image Perception

KEYWORDS: Image compression, Pathology, Radiography, Computing systems, Diagnostics, Receivers, Bone, Medical imaging, Image quality, Digital imaging

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PROCEEDINGS ARTICLE | September 8, 1993

DDx: diagnostic assistance for the radiologist using PACS

Proc. SPIE. 1899, Medical Imaging 1993: PACS Design and Evaluation

KEYWORDS: Information fusion, Statistical analysis, Databases, Image segmentation, Diagnostics, Medical imaging, Information technology, Radiology, Picture Archiving and Communication System, Brain

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PROCEEDINGS ARTICLE | June 30, 1993

X window image extension standard: what it means to medical imaging

Proc. SPIE. 1897, Medical Imaging 1993: Image Capture, Formatting, and Display

KEYWORDS: Image compression, Visualization, Surgery, Image processing, Diagnostics, Medical imaging, Digital imaging, Image enhancement, Convolution, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | June 30, 1993

Early evaluation of MDIS workstations at Madigan Army Medical Center

Proc. SPIE. 1897, Medical Imaging 1993: Image Capture, Formatting, and Display

KEYWORDS: Image compression, Imaging systems, Databases, Diagnostics, Chromium, Image quality, Image retrieval, Radiology, Nonimpact printing, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | July 1, 1992

Layered approach to workstation design for medical image viewing

Proc. SPIE. 1654, Medical Imaging VI: PACS Design and Evaluation

KEYWORDS: Human-machine interfaces, Surgery, Imaging systems, Databases, Magnetic resonance imaging, Image processing, Interfaces, Medical imaging, Local area networks, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | May 1, 1992

Three-dimensional distortion model for magnetic resonance images

Proc. SPIE. 1653, Medical Imaging VI: Image Capture, Formatting, and Display

KEYWORDS: 3D acquisition, Surgery, Imaging systems, Databases, Magnetic resonance imaging, Magnetism, Distortion, 3D modeling, Computed tomography, 3D image processing

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PROCEEDINGS ARTICLE | May 1, 1992

Development of a prototypical PACS workstation based on the IBM RS6000 and the X window system

Proc. SPIE. 1653, Medical Imaging VI: Image Capture, Formatting, and Display

KEYWORDS: Human-machine interfaces, Visualization, Surgery, Image processing, Medical imaging, Software development, Zoom lenses, Convolution, Image display, Picture Archiving and Communication System

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SPIE Journal Paper | October 1, 1991

Lossy image compression for digital medical imaging systems

Wilhelm, Paul , Haynor, David , Kim, Yongmin , Riskin, Eve

OE Vol. 30 Issue 10

KEYWORDS: Image compression, Fractal analysis, X-ray computed tomography, Picture Archiving and Communication System, X-rays, Medical imaging, Quantization, Head, X-ray imaging, Image analysis

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PROCEEDINGS ARTICLE | August 1, 1990

Clinical determinants of PACS acceptance

Proc. SPIE. 1234, Medical Imaging IV: PACS Systems Design and Evaluation

KEYWORDS: Surgery, Imaging systems, Image processing, Medical research, Reliability, Computing systems, Diagnostics, Digital imaging, Radiology, Picture Archiving and Communication System

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One of the key determinants influencing how successfully a radiology department can convert from a conventional film-based environment to an exclusively digital imaging environment may be how well referring physician members of the hospital staff who are not radiologists endorse this new system. The benefits of Picture Archive and Communication Systems (PACS) to radiologists are becoming widely accepted and documented; however, physicians who interact with the radiology department represent an important user group whose views on PACS are less well understood. The acceptance of PACS by referring physicians (clinicians) may be critical to the overall utility ofPACS as well as a major drivingforce behind why a hospitalpurchases PACS. The degree to which referring physicians support PACS may be dependent upon many factors. This study identifies several aspects through the administration and analysis ofa survey which improve PACS acceptance by nonradiology physicians. It appears the more patients a referring physician sends to the radiology department, the more time a physician spends traveling to andfrom thefllmflle room retrievingfllms, and, the more interested a referring physician is about computers, the higher his interest is in PACS. If a referring physician believes that PACS will save him or her time, will reduce the incidence oflostfilms, or will cause performance of radiology exams or generation of reports to be more efficient, the referring physician appears more likely to support PACS and to make the initial time investment necessary to learn how PACS equipment operates. The factors which cause referring physicians to support PACS are principally: (1) the elimination oflost, misplaced, and checked outfllms, and (2) the elimination oftrips to and from thefile room. The major distractions ofthe technology are: (1) system reliability, and (2) reduced diagnostic capability. While the high cost ofPACS is also a distraction, it is not the predominant concern.

PROCEEDINGS ARTICLE | August 1, 1990

Development of a prototype electronic alternator for DIN/PACS environment and its evaluation

Proc. SPIE. 1234, Medical Imaging IV: PACS Systems Design and Evaluation

KEYWORDS: Human-machine interfaces, Sun, Databases, Image processing, Diagnostics, Chromium, Medical imaging, Image quality, Prototyping, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | August 1, 1990

PACS economics and the referring physician

Proc. SPIE. 1234, Medical Imaging IV: PACS Systems Design and Evaluation

KEYWORDS: Medicine, Statistical analysis, Surgery, Imaging systems, Medical research, Medical imaging, Telecommunications, Radiology, Systems modeling, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | August 1, 1990

Clinical evaluation of PACS workstations: methodology and results

Proc. SPIE. 1234, Medical Imaging IV: PACS Systems Design and Evaluation

KEYWORDS: Human-machine interfaces, Imaging systems, Databases, Magnetic resonance imaging, Silicon, Diagnostics, Image resolution, Medical imaging, Radiology, Picture Archiving and Communication System

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PROCEEDINGS ARTICLE | July 1, 1990

Lossy image compression for digital medical imaging systems

Proc. SPIE. 1232, Medical Imaging IV: Image Capture and Display

KEYWORDS: Image compression, X-ray computed tomography, X-rays, Control systems, Medical imaging, Head, Quantization, Fractal analysis, X-ray imaging, Picture Archiving and Communication System

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Image compression at rates of 10:1 or greater could make PACS much more responsive and economically attractive. This paper describes a protocol for subjective and objective evaluation of the fidelity of compressed/decompressed images to the originals and presents the results ofits application to four representative and promising compression methods. The methods examined are predictive pruned tree-structured vector quantization, fractal compression, the discrete cosine transform with equal weighting of block bit allocation, and the discrete cosine transform with human visual system weighting of block bit allocation. Vector quantization is theoretically capable of producing the best compressed images, but has proven to be difficult to effectively implement. It has the advantage that it can reconstruct images quickly through a simple lookup table. Disadvantages are that codebook training is required, the method is computationally intensive, and achieving the optimum performance would require prohibitively long vector dimensions. Fractal compression is a relatively new compression technique, but has produced satisfactory results while being computationally simple. It is fast at both image compression and image reconstruction. Discrete cosine iransform techniques reproduce images well, but have traditionally been hampered by the need for intensive computing to compress and decompress images. A protocol was developed for side-by-side observer comparison of reconstructed images with originals. Three 1024 X 1024 CR (Computed Radiography) images and two 512 X 512 X-ray CT images were viewed at six bit rates (0.2, 0.4, 0.6, 0.9, 1.2, and 1.5 bpp for CR, and 1.0, 1.3, 1.6, 1.9, 2.2, 2.5 bpp for X-ray CT) by nine radiologists at the University of Washington Medical Center. The CR images were viewed on a Pixar II Megascan (2560 X 2048) monitor and the CT images on a Sony (1280 X 1024) monitor. The radiologists' subjective evaluations of image fidelity were compared to calculations of mean square error (MSE), normalized mean square error (NMSE), percentage mean square error (PMSE), and fractal normalized mean square error (FMSE) for each compression method and bit rate.

Showing 5 of 52 publications

Conference Committee Involvement (33)

Image Processing

16 February 2019 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

16 February 2019 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

12 February 2018 | Houston, Texas, United States

Image Processing

11 February 2018 | Houston, Texas, United States

Image-Guided Procedures, Robotic Interventions, and Modeling Posters

14 February 2017 | Orlando, FL, United States

Showing 5 of 33 published special sections

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