Computational Physiology Lab Department of Computer Science University of Houston Houston, TX Spatiotemporal Reconstruction of the Breathing Function Duc Duong Advisor: Dr. Ioannis Pavlidis

2/12 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 , August, 2007

3/12 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

4/12 Stacking SegmentationRegistration Methodology - Overview Segmentation Temporal Registration Stacking y x t x y Reference frame x y Next temporal frame x y x y

5/12 Segmentation Temporal 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 images Real Motion = Evolution + Body motion Phase Correlation Registration

6/12 Segmentation Temporal 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

7/12 Segmentation Temporal Registration Validation Stacking Registration positions/orientations are checked against ground-truth values Manual Transform: Rot. Ѳ = Tran. tx = 4.40, ty = 2.24 Manual Transform: Rot. Ѳ = Tran. tx = 4.40, ty = 2.24 Auto Realignment: Rot. Ѳ = 16 Tran. tx = 5, ty = 2 Quantitative Analysis Auto Alignment: Rot. Ѳ = 16 Tran. tx = 5, ty = 2 Manual Transform: Rot. Ѳ = Tran. tx = 4.40, ty = 2.24 Manual Transform: Rot. Ѳ = Tran. tx = 4.40, ty = 2.24 Qualitative Analysis

8/12 Segmentation Temporal 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 Hand Segmentation

9/12 Preliminary Results Visualization of 3D cloud of heat changes

10/12 Applications Deliver the same information as virtual thermistor Normal Breathing Waveform Left nostril Mean temperature signal measure at left nostril Abnormal Airway Obstruction Left nostril

11/12 Applications Detect irregular breathing patterns A 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

12/12 Future Work Improve the image registration Improve the segmentation Compute the airflow velocity and the volume of exchanged gas Thank you Q & A