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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.

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spatiotemporal reconstruction of the breathing function

Spatiotemporal Reconstruction of the Breathing Function

Duc Duong

Advisor: Dr. IoannisPavlidis

motivation
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

a new approach spatiotemporal reconstruction
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
methodology overview
Methodology - Overview

Temporal Registration

Segmentation

Stacking

Registration

Segmentation

Reference frame

y

y

y

y

x

x

x

Next temporal frame

y

x

Stacking

t

x

methodology
Methodology

Temporal Registration

Segmentation

Stacking

  • To register thermal images to a fixed global reference frame
  • To retain only the evolution of heat signature at nostrils

Solution: Phase correlating the Laplacians of two input thermal images

Real Motion = Evolution + Body motion

Phase Correlation Registration

methodology1
Methodology

Temporal Registration

Segmentation

Stacking

  • To capture nostril region(s) whose spatial heat is changing by time
  • To constrain boundaries of captured regions in a temporaladvective relation

Solution: Level set equation and level set curve

validation
Validation

Temporal Registration

Segmentation

Stacking

Registration positions/orientations are checked against ground-truth values

Qualitative Analysis

Quantitative Analysis

Manual Transform:

Rot. Ѳ = 14.48

Tran. tx = 4.40, ty = 2.24

Auto Realignment:

Rot. Ѳ = 16

Tran. tx = 5, ty = 2

Auto Alignment:

Rot. Ѳ = 16

Tran. tx = 5, ty = 2

Manual Transform:

Rot. Ѳ = 14.48

Tran. tx = 4.40, ty = 2.24

validation1
Validation

Temporal Registration

Segmentation

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

Hand Segmentation

* R. Unnikrishnan and M. Hebert, “Measures of Similarity”, 7th IEEE Workshop on Applications of Computer Vision, January, 2005, pp. 394-400.

preliminary results
Preliminary Results
  • Visualization of 3D cloud of heat changes
applications
Applications
  • Deliver the same information as virtual thermistor

Normal Breathing Waveform

Abnormal Airway Obstruction

Mean temperature signal measure at left nostril

Left nostril

Left nostril

applications1
Applications
  • Detect irregular breathing patterns

A failure tissue part inside right nostril

Failure tissues

Failure tissues can not be

identified from 1D waveform

Abrupt breathing at right nostril

Left nostril

Right nostril

future work
Future Work
  • Improve the image registration
  • Improve the segmentation
  • Compute the airflow velocity and the volume of exchanged gas

Thank you

Q & A

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