Object modeling by registration of multiple range images

Chen, Y.; Medioni, Gérard

doi:10.1109/robot.1991.132043

Cited by 1,270 publications

(1,025 citation statements)

References 9 publications

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“…To the authors' knowledge, all shape-based intra-operative registration methods presented for laparoscopic interventions so far are rigid and rely on the Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992;Chen and Medioni, 1992) or one of its many variants (Lamata et al, 2009;Rauth et al, 2007;Su et al, 2009). …”

Section: Intra-operative Registration For Augmented Reality Guidancementioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

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One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilites by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

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Section: Intra-operative Registration For Augmented Reality Guidancementioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

View full text Add to dashboard Cite

show abstract

“…It was introduced by Chen and Medioni [8] and in a similar version by Besl and McKay [9]. It has gained wide spread popularity due to its simple formulation and low complexity.…”

Section: Increasing Motionmentioning

confidence: 99%

Real Time Surface Registration for PET Motion Tracking

et al. 2011

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Abstract. Head movement during high resolution Positron Emission Tomography brain studies causes blur and artifacts in the images. Therefore, attempts are being made to continuously monitor the pose of the head and correct for this movement. Specifically, our method uses a structured light scanner system to create point clouds representing parts of the patient's face. The movement is estimated by a rigid registration of the point clouds. The registration should be done using a robust algorithm that can handle partial overlap and ideally operate in real time. We present an optimized Iterative Closest Point algorithm that operates at 10 frames per second on partial human face surfaces.

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“…As the error metric, the point-to-point or point-to-plain distance of the matching unit is mainly used [13] [14] . Some other algorithms adopt such additional information as the reflectance (the reflection ratio of the laser ray) and color of the captured point as the error metric [15] .…”

Section: Error Metricmentioning

confidence: 99%