Spatiotemporal reconstruction of the breathing function
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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

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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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