image processing group: research activities in medical image
TRANSCRIPT
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Image Processing Group: Research
Activities in Medical Image Analysis
Sven Lončarić
Faculty of Electrical Engineering and Computing
University of Zagreb
http://www.fer.unizg.hr/ipg
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
• Marko Subašić, Assistant Professor
• Tomislav Petković, postdoctoral fellow
• Hrvoje Kalinić, doctoral student
• Vedrana Baličević, doctoral student
• Adam Heđi, former graduate student
• Hrvoje Bogunović, former graduate student
• Tomislav Devčić, former graduate student
Image Processing Group Members
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
IPG
members
sailing on
Adriatic
coast,
2006
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
6th Int'l Symposium on Image and Signal Processing and Analysis
September 4-6, 2011, Dubrovnik, Croatia
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Medical image analysis projects
• Some example projects:
– Aortic outflow velocity Doppler ultrasound image analysis
– Detection and tracking of catheter for intravascular
interventions
– 3-D analysis of abdominal aortic aneurysm
– 3-D analysis of intracerebral brain hemorrhage
– Virtual endoscopy
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Aortic outflow velocity profile analysis
• Partners:
– Hrvoje Kalinić, Sven Lončarić,
FER
– Maja Čikeš, Davor Miličić,
University Hospital Rebro
– Bart Bijnens, Pompeu Fabra
University Barcelona
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Signal interpretation
Automatic signal
extraction
Manual indication of ejection time
Signal modeling
Signal feature
extraction
HDF image
Aortic outflow velocity analysis method
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Aortic outflow velocity profile
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
• Atlas construction
• Segmentation propagation • Root
image
Atlas-based segmentation
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Signal modelling used for feature
extraction
Extracted image: Doppler envelope detection, ET selection
Original signal
fall time (tfall)䇱 time from 90% to 10%
of descending trace value
asymmetry factor (asymm) = (P1-P2)/Poverall
the difference of area under the curve of left and right
half of the spectrum normalized by the overall area
rise time (trise)䇱 time from 10% to 90%
of ascending trace value
P1 P2
ETmod
Ejection time (ETmod
)䇱 Time from onset to peak aortic flow (T
mod)䇱
Tmod
Atlas-based segmentation
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
No evidence of CAD, negative DSE
Typical normal trace, triangular in shape, with the peak ocurring early
• Shortened Tmod/ETmod
• Prolonged tfall
• Asymmetric curve
T_mat
ET_mat
CAD negative
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Typical broadening with a much more rounded shape and later peak
Severe CAD, positive DSE
• Prolonged Tmod/ETmod
• Prolonged trise
• Shortened tfall
• Symmetric curve
T_mat
ET_mat
Severe CAD
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Real-Time Guidewire Tracking
! Project team:
! Sven Lončarić, Tomislav Petković,
Tomislav Devčić, University of Zagreb
! Draženko Babić, Robert Homan,
Philips Healthcare
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Problem statement
! Automated guidewire tracking system should provide the surgeon with the information about 3D guidewire position in real-time during the intravascular intervention.
! If possible the simplest monoplane X-ray imaging device should be used.
! Develop smart software to extend usability of existing expensive hardware
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Overview
C-arm
X-ray image
guidewire
Pathology:
• Aneurysim
• Stenosis
endovascular coiling
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Achieved results
! A prototype system was developed
! Processing time is about 100 ms per image
of 1024x1024 with 16 bits resolution
! Reconstruction from single image is possible,
but yields many ambiguous solutions
! Reconstruction from two views (biplane) is
also ambiguous
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System overview
(monoplane reconstruction)
! 3D position reconstruction is desirable
! Ambiguous solutions exist due to the projective
nature of imaging device
! All viable solutions are found and most probable
one is selected as reconstruction result
! Fast minimization algorithms are required due to
real-time constraints
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Software demonstration
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Abdominal Aortic Aneurysm (AAA)
• Project on AAA segmentation from CT
images
• Partners:
– Marko Subašić, Sven Lončarić, University of
Zagreb
– Erich Sorantin, Medical University Graz, Austria
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Abdominal Aortic Aneurysm (AAA)
• Enlargement of
abdominal aorta due to
weakened aortic wall
• Enlargement of aorta can
lead to aortic wall rapture
• Imaging of AAA is very
important in condition
assessment Abdominal aorta With aneurysm
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AAA segmentation method
• Abdominal volume CT input data
• Manual segmentation??
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Geometric deformable model
• Ability to change topology: break and merge
• Easy to build numerical approximation of equations of motion
• Straightforward expansion to higher dimensions 3-D, 4-D ...
• Level-set algorithm
γ
Ψ
Picture plane x d
Ψ(x)
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The problem
• Two regions of interest:
1. Aortic interior • Good image conditions – not a
difficult task
2. Aortic wall • Poor image conditions on outer aortic
border – a more difficult task
• Calcification: a sediment of calcium inside aortic wall
• Barely visible outer aortic border
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Deformable model for AAA
Input: spiral CT
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Results
relative error [%]
standard deviation
[%]
automatic level-set (corrected automatic
segmentation results) 14.71 8.17
automatic level-set (corrected semi-automatic
segmentation) 19.75 13.28
corrected automatic segmentation
(corrected semi-automatic segmentation)
12.35 13.92
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ICH segmentation from CT images
• Project: Segmentation of intracerebral brain
hemorrhage from CT images
• Goal: quantitative analysis of hematoma and
edema
• Partners:
– University of Cincinnati Medical Center, USA
– University of Zagreb
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Expert system segmentation
clustering Expert system
for labeling
CT image Labeled
image
• Segmentation by clustering breaks image into
small regions
• Expert system has knowledge about size, shape
and neighborhood relations between regions and
uses this knowledge for region labeling
• Labels: hematoma, edema, brain, skull,
background
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Experimental results
CT brain image
Segmented regions:
background, skull,
brain, hematoma
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Artificial neural networks
• Can be used for analysis of biomedical images
• Block diagram shows alternative methods for ICH
image analysis
K-means clustering for
brightness normalization
Receptive field for
feature extraction
Neural network for
pixel classification
Expert system
for region
labeling
Input image
Output image
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Artificial neural networks
• ANNs can be used as classifiers
• Receptive field
Multi-layer
neural network
Pixel label
CT image
Circular receptive field
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Results
input image segmented regions labeled regions
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Virtual endoscopy
• Virtualna endoskopija provodi se: – 3-D imaging of human body (CT, MR)
– image analysis to determine organ position
– patient-specific 3-D model for interactive exploration
• Advantages of virtual endoscopy: – less invasive then classical endoscopy
– Unlimited moving and positioning of virtual endoscope
– fly-through and interactive 3-D visualizations
• Examples: virtual colonoscopy, virtual bronchoscopy, colon “unwrapping”
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Virtual bronchoscopy
• 3D modeling of organs
• Fly-through simulations
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Conclusion
• Computerized medical imaging and image processing can aid clinical research, diagnostics, and
intervention
• Interdisciplinary projects require interdisciplinary
teams: doctors and engineers
• Computer: A tool for quantitative measurements of organ morphology and function
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Image Processing Group, Faculty of Electrical Engineering & Computing, Univ. of Zagreb
Thank you for your attention
Contact: Professor Sven Lončarić
Faculty of Electrical Engineering and Computing
Department for Electronic Systems and Information Processing
Image Processing Group
E-mail: [email protected]
WWW: http://ipg.zesoi.fer.hr
Office phone: +385-1-6129-891