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 correlating 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

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..,,...,,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