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Markerless estimation of patient orientation, posture and pose using range and pressure imaging

For automatic patient setup and scanner initialization in tomographic imaging
  • Robert Grimm
  • Sebastian Bauer
  • Johann Sukkau
  • Joachim Hornegger
  • Günther Greiner
Original Article

Purpose

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.

Methods

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.

Results

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).

Conclusions

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.

Keywords

Range imaging Pressure imaging Diagnostic tomographic imaging Posture classification Pose estimation 

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Copyright information

© CARS 2012

Authors and Affiliations

  • Robert Grimm
    • 1
    • 3
  • Sebastian Bauer
    • 1
  • Johann Sukkau
    • 2
  • Joachim Hornegger
    • 1
  • Günther Greiner
    • 3
  1. 1.Pattern Recognition Lab, Graduate School in Advanced Optical Technologies (SAOT)Friedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.Siemens AG, Healthcare SectorErlangenGermany
  3. 3.Chair of Computer GraphicsFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany

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