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Spatiotemporal Reconstruction of the Breathing Function

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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  1. Spatiotemporal Reconstruction of the Breathing Function Duc Duong Advisor: Dr. IoannisPavlidis

  2. 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

  3. 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

  4. 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

  5. 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 correlation of the Laplacians of two input thermal images Real Motion = Evolution + Body motion Phase Correlation Registration

  6. 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

  7. 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

  8. 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.

  9. Preliminary Results • Visualization of 3D cloud of heat changes

  10. 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

  11. 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

  12. 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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