Ms. Anaïs Chambrial Profile

Anaïs Chambrial

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 30 March 2020 Presentation + Paper

Non-rigid registration of 3D points clouds of deformed liver models with Open3D and PyCPD

Audrey Leong-Hoï, Anaïs Chambrial, Marine Collet, Jean-Pierre Radoux

Proceedings Volume 11351, 113511N (2020) https://doi.org/10.1117/12.2555673

KEYWORDS: Clouds, Liver, 3D modeling, Image registration, Rigid registration, Image processing, Detection and tracking algorithms, Cameras, Augmented reality, Statistical modeling

Read 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.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years