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Center forSubsurface Sensing & Imaging Systems
Center forSubsurface Sensing & Imaging Systems
Overview of Image and Data Information
Management in CenSSIS
David Kaeli Northeastern University
Boston, [email protected]
R1
R2
Overview of the Strategic Research PlanOverview of the Strategic Research Plan
FundamentalScienceFundamentalScience
ValidatingTestBEDsValidatingTestBEDs
L1L1
L2L2
L3L3
R3Image and Data
InformationManagement
S1 S4 S5S3S2
Bio-Med Enviro-Civil
R3 Research Thrust OverviewR3 Research Thrust Overview
Utilize enabling hardware and software technologies to address CenSSIS barriers
Pursue research in enabling technologies Develop a common set of tools and techniques to
address SSI problems: Hardware parallelization and acceleration Software toolboxes Image database management and tools
Utilize enabling hardware and software technologies to address CenSSIS barriers
Pursue research in enabling technologies Develop a common set of tools and techniques to
address SSI problems: Hardware parallelization and acceleration Software toolboxes Image database management and tools
Toolboxes
CenSSIS Middleware Tools CenSSIS Middleware Tools
Parallelization of MATLAB, C/C++ and Fortran codes using Message Passing Interface (MPI) – a software pathway to exploiting GRID-level resources
Utilizing MPI-2 to address barriers in I/O performance Building on existing Grid Middleware such as Globus
Toolkit, MPICH-G2 and GridPort Presently illustrating the impact of the GRID on system
level projects (tomosynthesis reconstruction)
Parallelization of MATLAB, C/C++ and Fortran codes using Message Passing Interface (MPI) – a software pathway to exploiting GRID-level resources
Utilizing MPI-2 to address barriers in I/O performance Building on existing Grid Middleware such as Globus
Toolkit, MPICH-G2 and GridPort Presently illustrating the impact of the GRID on system
level projects (tomosynthesis reconstruction)
MATLAB
C/C++
Fortran
Parallelization
MPI
MPICH-G2
UPC
Impact on CenSSIS ApplicationsImpact on CenSSIS Applications
Reduced the runtime of a single-body Steepest Descent Fast Multipole Method (SDFMM) application by 74% on a 32-node Beowulf cluster• Hot-path parallelization• Data restructuring
Reduced the runtime of a Monte Carloscattered light simulation by 98% on a 16-node Silicon Graphics Origin 2000• Matlab-to-C compliation• Hot-path parallelization
• Obtained superlinear speedup of Ellipsoid Algorithm run on a 16-node IBM SP2 • Matlab-to-C compliation• Hot-path parallelization
Reduced the runtime of a single-body Steepest Descent Fast Multipole Method (SDFMM) application by 74% on a 32-node Beowulf cluster• Hot-path parallelization• Data restructuring
Reduced the runtime of a Monte Carloscattered light simulation by 98% on a 16-node Silicon Graphics Origin 2000• Matlab-to-C compliation• Hot-path parallelization
• Obtained superlinear speedup of Ellipsoid Algorithm run on a 16-node IBM SP2 • Matlab-to-C compliation• Hot-path parallelization
Soil
Air
Mine
Scattered Light Simulation Speedup
1
10
100
1000
10000
100000
Ru
nti
me
in s
ec
on
ds
Original
Matlab-to-C
Hot pathparallelization
Ellipsoid Algorithm Speedup(versus serial C version)
05
101520
1 2 4 8 16
Number of Nodes
Sp
ee
du
p
64-vector 256-vector1024-vector linear speedup
Tomographic mammographyTomographic mammography
3D image reconstruction from x-ray projections Used to detect and diagnose breast cancer Based on well developed mammography techniques Exposes tissue structure using multiple projections from different angles
Advantages Accuracy: provides at least as much useful information than x-ray film Flexibility: digital image manipulation, digital storage Structural information: using layered images Safe: low-dose x-ray Lower cost: compared to MRI
3D image reconstruction from x-ray projections Used to detect and diagnose breast cancer Based on well developed mammography techniques Exposes tissue structure using multiple projections from different angles
Advantages Accuracy: provides at least as much useful information than x-ray film Flexibility: digital image manipulation, digital storage Structural information: using layered images Safe: low-dose x-ray Lower cost: compared to MRI
Image acquisition/reconstruction processImage acquisition/reconstruction process
Acquisition: 11 uniform angular samples along Y-axis X-ray projection: breast tissue density absorption radiograph Algorithm: constrained non-linear convergence and iterative process Uses a Maximum Likelihood Estimation
Acquisition: 11 uniform angular samples along Y-axis X-ray projection: breast tissue density absorption radiograph Algorithm: constrained non-linear convergence and iterative process Uses a Maximum Likelihood Estimation
detector
X-ray source
X
Z
Y
Y
Set 3D volume
Compute projections
Correct 3D volume
3D volume
Satisfied ?NoYes
Exit
Initialization
Forward
Backward
X-rayprojections
Parallelization approachesParallelization approaches
Reduce communication data Segmentation along Y-axis Using redundant computation to replace communication Segmenting along x-ray beam
Reduce communication data Segmentation along Y-axis Using redundant computation to replace communication Segmenting along x-ray beam
First approach:Non inter-communication(more computation, less communication)
Second approach:Overlap with inter-communication
Third approach:Non-overlap with inter-communication(less computation, morecommunication)
exchange dataOverlap area
Tomosynthesis AccelerationTomosynthesis Acceleration
Phantom data test results using non-overlap method on 32 CPUs
0
50
100
150
200
250
300
350
P4 cluster Hypnoscluster
Titancluster
IBM p690 SGI Altix
Platform
Tim
e (s
ec)
File IOCollectInter-commSyncBackwardForward
•Input data set: phantom 1600x2034x45
• Serial implementations runs in 2-3 hours on a P4 machine
• Platforms:
– SGI Altix system
– UIUC NCSA Titan cluster
– UIUC NCSA IBM p690
– UMich Hypnos cluster
– P4 cluster at MGH
• Number of processors: 32
Computation: SGI Altix with Itanium 2 processor outperforms the other CPUs
Currently moving this work to the GRID and the Pittsburgh Supercomputer Center
Prototype running on our GRID system at NU
Field Programmable Gate Arrays for Subsurface ImagingField Programmable Gate Arrays for Subsurface Imaging
Backprojection for Computed Tomography image reconstruction Sponsored by Mercury Computer
Finite Difference Time Domain (FDTD) in hardware Collaboration with Humanitarian Demining project
Retinal Vascular Tracing in real time Collaboration with Real-time Retinal Imaging project
Phase Unwrapping Collaboration with 3-D Fusion Microscope project
Diverse problems, similar solutions:
FPGAs are particularly well suited for accelerating image processing and image understanding algorithms
Backprojection for Computed Tomography image reconstruction Sponsored by Mercury Computer
Finite Difference Time Domain (FDTD) in hardware Collaboration with Humanitarian Demining project
Retinal Vascular Tracing in real time Collaboration with Real-time Retinal Imaging project
Phase Unwrapping Collaboration with 3-D Fusion Microscope project
Diverse problems, similar solutions:
FPGAs are particularly well suited for accelerating image processing and image understanding algorithms
Retinal Vascular Tracing: Register 2-D Image to 3-D in Real TimeRetinal Vascular Tracing: Register 2-D Image to 3-D in Real Time
FIREBIRD BOARD
HOST
Direction ofblood vessel
PCI BUS
ObjectiveTo accelerate an existing retinalvascular tracing (RVT) algorithm byimplementing computation of templateresponses in reconfigurable hardware
FPGA
BL
OC
KR
AM
DESIGN
MEMORY0
IMAGEMEMORY1
RESULTS
“Smart Camera”
Direction of blood vessel
PCI BUS
Some Recent Publications on ParallelizationSome Recent Publications on Parallelization
• “Execution-Driven Simulation of Network Storage Systems,” Y. Wang and D. Kaeli, Proceedings of the 12th ACM/IEEE International Symposium on Modeling, Analysis of Computer and Telecommunication Systems, October 2004, pp. 604-611.
• “Profile-guided File Paritioning on Beowulf Clusters,” Y. Wang and D. Kaeli, Journal o f Cluster Computing, Special Issue on Parallel I/O, to appear,
• “An Object-oriented Parallel Library,” C. Oaurrauri and D. Kaeli, International Journal of High Performance of Computing and Networking, to appear.
• “Digital Tomosynthesis Mammography using a Parallelized Maximum Likelihood Reconstruction Method,” T. Wu, R. Moore, E. Rafferty, D. Koppans, J. Zhang, W. Meleis and D. Kaeli, Medical Imaging, 5368, February 2004.
• “Mapping and characterization of applications in Heterogeneous Distributed Systems,” J. Yeckle and W. Rivera , To appear in Proceed. of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI2003).
• “Profile-Guided I/O Partitioning,” Y. Wang and D. Kaeli, Proceedings of the 17th ACM International Symposium on Supercomputing, June 2003, pp. 252-260.
• “Source-Level Transformations to Apply I/O Data Partitioning,” Y. Wang and D. Kaeli, Proceedings of the IEEE Workshop on Storage Network Architecture and Parallel IO, Oct. 2003, pp. 12-21.
• “Execution-Driven Simulation of Network Storage Systems,” Y. Wang and D. Kaeli, Proceedings of the 12th ACM/IEEE International Symposium on Modeling, Analysis of Computer and Telecommunication Systems, October 2004, pp. 604-611.
• “Profile-guided File Paritioning on Beowulf Clusters,” Y. Wang and D. Kaeli, Journal o f Cluster Computing, Special Issue on Parallel I/O, to appear,
• “An Object-oriented Parallel Library,” C. Oaurrauri and D. Kaeli, International Journal of High Performance of Computing and Networking, to appear.
• “Digital Tomosynthesis Mammography using a Parallelized Maximum Likelihood Reconstruction Method,” T. Wu, R. Moore, E. Rafferty, D. Koppans, J. Zhang, W. Meleis and D. Kaeli, Medical Imaging, 5368, February 2004.
• “Mapping and characterization of applications in Heterogeneous Distributed Systems,” J. Yeckle and W. Rivera , To appear in Proceed. of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI2003).
• “Profile-Guided I/O Partitioning,” Y. Wang and D. Kaeli, Proceedings of the 17th ACM International Symposium on Supercomputing, June 2003, pp. 252-260.
• “Source-Level Transformations to Apply I/O Data Partitioning,” Y. Wang and D. Kaeli, Proceedings of the IEEE Workshop on Storage Network Architecture and Parallel IO, Oct. 2003, pp. 12-21.
Held again
in 2004
CenSSIS Solutionware – UPRM/NU/RPICenSSIS Solutionware – UPRM/NU/RPI
Toolbox Development Support the development of CenSSIS Solutionware that
demonstrates our “Diverse Problems – Similar Solutions” model
Develop Toolboxes that support research and education Establish software development and testing standards for
CenSSIS
Image and Sensor Data Database Develop an web-accessible image database for CenSSIS that
enables efficient searching and querying of images, metadata and image content
Develop image feature tagging capabilities
Toolbox Development Support the development of CenSSIS Solutionware that
demonstrates our “Diverse Problems – Similar Solutions” model
Develop Toolboxes that support research and education Establish software development and testing standards for
CenSSIS
Image and Sensor Data Database Develop an web-accessible image database for CenSSIS that
enables efficient searching and querying of images, metadata and image content
Develop image feature tagging capabilities
Toolboxes
Status of the CenSSIS ToolboxesStatus of the CenSSIS Toolboxes
Hyperspectral Image Analysis Toolbox (HIAT) October 2004
Multiview Tomography Toolbox (MVT) fddlib:
January 2003 (v. 1.0) July 2003 (v. 1.1)
mvt: October 2004
Rensselaer Generalized Registration Library (RGRL) September 2004
Hyperspectral Image Analysis Toolbox (HIAT) October 2004
Multiview Tomography Toolbox (MVT) fddlib:
January 2003 (v. 1.0) July 2003 (v. 1.1)
mvt: October 2004
Rensselaer Generalized Registration Library (RGRL) September 2004
HIAT
MVT
RGRL
New toolbox: Improving the quality of radiation oncology @ MGHNew toolbox: Improving the quality of radiation oncology @ MGH
Developed a 4D (3D + including time) visualization browser tool kit
Visualize Computed Tomography (CT) images, organ outlines (wire contours) and the isodose lines (treatment dosage)
Present all this information in a user friendly interface
Developed a 4D (3D + including time) visualization browser tool kit
Visualize Computed Tomography (CT) images, organ outlines (wire contours) and the isodose lines (treatment dosage)
Present all this information in a user friendly interface
4-D Visualization of Lung Tumors4-D Visualization of Lung Tumors
Dosage
4-D Visualization
The Future for CenSSIS ToolboxesThe Future for CenSSIS Toolboxes
SCIRun
Collaboration with the University of Utah
Deliver an web-accessible database forCenSSIS that enables efficient searching and querying of images, sensor data, metadata and image content
More that 4000 metadata-rich images/datasets presently available online (> 10,000 by 2006)
Database Characteristics:
• Relational complex queries (Oracle9i)
• Data security, reliability and layered user privileges
• Efficient search and query of image content and metadata
• Content-based image tagging using XML
• Indexing algorithms (2D, 3D, and 4D)
• Explore object relational technology to handle collections
CenSSIS Image Database SystemCenSSIS Image Database System
12
34
mouse embryo
CenSSIS Image Database SystemCenSSIS Image Database System
CenSSIS Image Database SystemCenSSIS Image Database System
CenSSIS Image Database SystemCenSSIS Image Database System
Utilize Machine Learning
algorithms to improve query
view
CenSSIS Image Database SystemCenSSIS Image Database System
Provides data
description associated with initial collection,
but does not allow for further
elaboration or
annotation.
Image AnnotationImage Annotation
Provide the ability to markup image with searchable features
Enable image database to be more effectively data-mined
Provide the ability to markup image with searchable features
Enable image database to be more effectively data-mined
<xml version=“1.0” encoding=“UTF-8”>
<embryo>
<description> Embryo developmental stages</description>
<feature label=“1” xPos1=“29” yPos1=“33” xPos2=“48” yPos2=“50”> 1 cell embryo </feature>
<feature label=“2” xPos1=“50” yPos1=“28” xPos8=“70” yPos2=“40”> 2 cell embryo
</feature>
<feature label=“3” xPos1= “5” yPos1= “5” xPos2=“25 yPos2=“20”> 4 cell embryo </feature>
</embryo>
XML and JavaXML and Java
• XML (Extensible Markup Language)
• Provides maximum flexibility and portability
• Well-supported standard
• Powerful querying tools available in Oracle
• The Java2 Platform
• Cross-platform compatibility
• Standard web-browser interface
• Native XML support
Image TaggingImage Tagging
A raw image file from the CenSSIS Database
• QUERY: I want to be able to add to this image textual annotations, providing my medical team with questions about particular ROIs:
• Difficult to describe regions in an image
• Difficult to pinpoint specific features in images
• Global image metadata too coarse to facilitate low-level tagging
Image TaggingImage Tagging
Image with tags
• Metadata associated with specific areas
• Query for specific image features
The Image Tagging InterfaceThe Image Tagging Interface
Drawing Tools
View Options
Tag Options
Tags and XMLTags and XML
<feature type="Ellipse" label="4 Cell Stage"> <ellipse> <xCenter> 101 </xCenter> <yCenter> 58 </yCenter> <xRadius> 79 </xRadius> <yRadius> 46 </yRadius> </ellipse> <note> [custom XML tags go here] </note> <annotator> awilliam </annotator> </feature>
The Future Role of Image AnnotationThe Future Role of Image Annotation
Provide a vehicle for natural collaboration
• A richer set of metadata to enable more detailed queries
• Potential to perform extensive data mining on image content
• An eye toward content-based image retrieval
Tumor tracking paper recently accepted to SIGMOD 2005
The CenSSIS Image Database SystemThe CenSSIS Image Database System
Hosts the image and sensor data of CenSSIS (>500 images online) http://censsis-db1.ece.neu.edu/
Provides metadata indexed image searching Uses XML tags to allow for easy information interchange Evolved into a project-based management system, allowing
users to organize their data hierarchically Key issue: how do we develop collaboration tools that
increase the value of data stored in the database? Presently exploring how best to integrate both visualization
and image annotation into the existing framework (NIH proposal)
Hosts the image and sensor data of CenSSIS (>500 images online) http://censsis-db1.ece.neu.edu/
Provides metadata indexed image searching Uses XML tags to allow for easy information interchange Evolved into a project-based management system, allowing
users to organize their data hierarchically Key issue: how do we develop collaboration tools that
increase the value of data stored in the database? Presently exploring how best to integrate both visualization
and image annotation into the existing framework (NIH proposal)
CenSSIS Image and Data Information Management CenSSIS Image and Data Information Management
Addressing key research barriers in computational efficiency, embedded computing and image/sensor data management
Exploiting Grid resources to enable new discovery in SSI applications
Producing a image/data repository and software-engineered Subsurface Sensing and Imaging Toolsets
Developing enabling tools targeting system-level projects • Near real-time reconstruction and visualization• Visualization of complex motion• Predicting motion in image data using database indexing
techniques
Addressing key research barriers in computational efficiency, embedded computing and image/sensor data management
Exploiting Grid resources to enable new discovery in SSI applications
Producing a image/data repository and software-engineered Subsurface Sensing and Imaging Toolsets
Developing enabling tools targeting system-level projects • Near real-time reconstruction and visualization• Visualization of complex motion• Predicting motion in image data using database indexing
techniques
MVT