Markerless estimation of patient orientation, posture and pose using range and pressure imaging
- 198 Downloads
In diagnostic tomographic imaging, patient setup and scanner initialization is a manual, tedious procedure in clinical practice. A fully-automatic detection of the patient’s position, orientation, posture and pose on the patient table holds great potential for optimizing this part of the imaging workflow. We propose a markerless framework that is capable of extracting this information within seconds from either range imaging (RI) or pressure imaging (PI) data.
The proposed method is composed of three stages: First, the position and orientation of the reclined patient are determined. Second, the patient’s posture is classified. Third, based on the estimated orientation and posture, an approximate body pose is recovered by fitting an articulated model to the observed RI/PI data. Being a key issue for clinical application, our approach does not require an initialization pose.
In a case study on real data from 16 subjects, the performance of the proposed system was evaluated quantitatively with a 3-D time-of-flight RI camera and a pressure sensing mattress (PI). The patient orientation was successfully determined for all subjects, independent of the modality. At the posture recognition stage, our method achieved mean classification rates of 79.4% for RI and 95.5% for PI data, respectively. Concerning the approximate body pose estimation, anatomical body landmarks were localized with an accuracy of ±5.84cm (RI) and ±5.53cm (PI).
The results indicate that an estimation of the patient’s position, orientation, posture and pose using RI and PI sensors, respectively, is feasible, and beneficial for optimizing the workflow in diagnostic tomographic imaging. Both modalities achieved comparable pose estimation results using different models that account for modality-specific characteristics. PI outperforms RI in discriminating between prone and supine postures due to the distinctive pressure distribution of the human body.
KeywordsRange imaging Pressure imaging Diagnostic tomographic imaging Posture classification Pose estimation
Unable to display preview. Download preview PDF.
- 1.Keil A, Wachinger C, Brinker G, Thesen S, Navab N (2006) Patient position detection for SAR optimization in magnetic resonance imaging. In: Proceedings of international conference on medical image computing and computer assisted intervention, pp 49–57Google Scholar
- 2.Wachinger C, Mateus D, Keil A, Navab N (2010) Manifold learning for patient position detection in MRI. In: Proceedings of IEEE international symposium on biomedical imaging, pp 1353–1356Google Scholar
- 3.Fenchel M, Thesen S, Schilling A (2008) Automatic labeling of anatomical structures in MR FastView images using a statistical atlas. In: Proceedings of international conference on medical image computing and computer assisted intervention, pp 576–584Google Scholar
- 8.Haker M, Böhme M, Martinetz T, Barth E (2009) Self-organizing maps for pose estimation with a time-of-flight camera. In: Proceedings of DAGM dynamic 3D imaging workshop, pp 142–153Google Scholar
- 9.Jensen R, Paulsen R, Larsen R (2009) Analysis of gait using a treadmill and a time-of-flight camera. In: Proceedings of DAGM dynamic 3D imaging workshop, pp 154–166Google Scholar
- 11.Zhu Y, Dariush B, Fujimura K (2008) Controlled human pose estimation from depth image streams. In: Proceedings of IEEE conference on computer vision and pattern recognition workshops, pp 1–8Google Scholar
- 12.Schaller C, Rohkohl C, Penne J, Stürmer M, Hornegger J (2009) Inverse C-arm positioning for interventional procedures using real-time body part detection. In: Proceedings of international conference on medical image computing and computer assisted intervention, pp 549–556Google Scholar
- 13.Seo KH, Oh C, Lee JJ (2004) Intelligent bed robot system: pose estimation using sensor distribution mattress. In: Proceedings of IEEE international conference on robotics and biomimetics, pp 828–832Google Scholar
- 14.Harada T, Mori T, Nishida Y, Yoshimi T, Sato T (1999) Body parts positions and posture estimation system based on pressure distribution image. In: Proceedings of IEEE international conference on robotics and automation, vol 2, pp 968–975Google Scholar
- 15.Harada T, Sato T, Mori T (2001) Pressure distribution image based human motion tracking system using skeleton and surface integration model. In: Proceedings IEEE international conference on robotics and automation, vol 4, pp 3201–3207Google Scholar
- 16.Kolb A, Barth E, Koch R, Larsen R (2009) Time-of-flight sensors in computer graphics. In: Eurographics, pp 119–134Google Scholar
- 17.Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley, Reading, MAGoogle Scholar
- 18.Brefeld U, Gärtner T, Scheffer T, Wrobel S (2006) Efficient co-regularised least squares regression. In: Proceedings of international conference on machine learning, ACM, pp 137–144Google Scholar