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30 March 2020 Non-rigid registration of 3D points clouds of deformed liver models with Open3D and PyCPD
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Abstract
Medical field has always benefited from the latest technological headways such as radiography, robotics or more recently augmented reality. Indeed, the progress in image analysis and augmented reality have led to major therapeutic progress in the surgical field as well as in the diagnosis field. Thus, one of the most important technique of medical image analysis is the registration. Image registration is the process of matching two or more images. More concretely, it consists in finding the transformation that minimizes the difference between two or more images. The transformation can be rigid (composed of rotations and translations only), affine (composed of rotations, translations and scales), or non-rigid. Even though rigid registration can seem quite easy to perform, developing and implementing solutions that realize fast, precise and robust rigid registration on complex objects is still challenging, especially when we deal with 3D objects. One of the most known and used rigid-registration algorithm is the Iterative Closest Point algorithm that has been implemented notably by the Open3D library. However, this method was unable to handle non-rigid registration. That is the reason why we have decided to use the Coherent Point Drift algorithm with non-rigid deformations. To this end, we have used the PyCPD library. In this paper, we present an efficient method for non-rigid registration applied to deformed liver models, robust to translations, rotations and cropping even though it fails to handle the most complex cases.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Audrey Leong-Hoï, Anaïs Chambrial, Marine Collet, and Jean-Pierre Radoux "Non-rigid registration of 3D points clouds of deformed liver models with Open3D and PyCPD", Proc. SPIE 11351, Unconventional Optical Imaging II, 113511N (30 March 2020); https://doi.org/10.1117/12.2555673
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