spatiotemporal reconstruction of the breathing function
DESCRIPTION
Spatiotemporal Reconstruction of the Breathing Function. Duc Duong Advisor: Dr. Ioannis Pavlidis. Motivation. A need of a less obtrusive sleep study Virtual thermistor * Preserves the temporal component: breathing waveform and rate Loses spatial heat distribution. - PowerPoint PPT PresentationTRANSCRIPT
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Computational Physiology LabDepartment of Computer Science
University of HoustonHouston, TX 77004
Spatiotemporal Reconstruction of the Breathing Function
Duc DuongAdvisor: Dr. Ioannis Pavlidis
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Motivation
• A need of a less obtrusive sleep study
• Virtual thermistor*
– Preserves the temporal component: breathing waveform and rate
– Loses spatial heat distribution
* J. Fei and I. Pavlidis, “Virtual thermistor”, Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 250-3, August, 2007
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A New Approach – Spatiotemporal Reconstruction
– Preserve spatial heat distribution at nostrils (or heat signature)
– Temporal evolution (or changes) of heat signature’s boundaries
– More information to clinical need
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Stacking
SegmentationRegistration
Methodology - Overview
SegmentationTemporal Registration Stacking
y
x
t
x
yReference frame
x
yNext temporal frame
x
yx
y
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SegmentationTemporal Registration
• To register thermal images to a fixed global reference frame• To retain only the evolution of heat signature at nostrils
Methodology
Stacking
Solution: Phase correlation of the Laplacians of two input thermal imagesReal Motion = Evolution +
Body motion
Phase Correlation Registration
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SegmentationTemporal Registration
• To capture nostril region(s) whose spatial heat is changing by time• To constrain boundaries of captured regions in a temporal advective relation
Methodology
Stacking
Solution: Level set equation and level set curve
)(
..,,...,,1
10
utt
n ts
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SegmentationTemporal Registration
Validation
Stacking
Registration positions/orientations are checked against ground-truth values
Manual Transform: Rot. = 14.48ѲTran. tx = 4.40, ty = 2.24
Auto Realignment: Rot. = 16ѲTran. tx = 5, ty = 2
Quantitative Analysis
Auto Alignment: Rot. = 16ѲTran. tx = 5, ty = 2
Manual Transform: Rot. = 14.48ѲTran. tx = 4.40, ty = 2.24
(.,.)f
Qualitative Analysis
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SegmentationTemporal Registration
Validation
Stacking
• Six ground-truth sets of hand segmentation by three experts• Make use of PRI (Probability Rand Index*) to measure a consistency between auto-segmentation and ground-truth sets
* R. Unnikrishnan and M. Hebert, “Measures of Similarity”, 7th IEEE Workshop on Applications of Computer Vision, January, 2005, pp. 394-400.
Hand Segmentation
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Preliminary Results
• Visualization of 3D cloud of heat changes
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Applications
• Deliver the same information as virtual thermistorNormal Breathing Waveform
Left nostril
Mean temperature signal measure at left nostril
Abnormal Airway Obstruction
Left nostril
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Applications
• Detect irregular breathing patternsA failure tissue part inside right nostril
Failure tissues
Failure tissues can not be identified from 1D waveform
Left nostril
Right nostril
Abrupt breathing at right nostril
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Future Work
• Improve the image registration• Improve the segmentation• Compute the airflow velocity and the volume of
exchanged gas
Thank youQ & A
•