Mr. Lingxi Guo Profile

Lingxi Guo

SPIE Involvement:

Author

Publications (2)

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 April 2024 Paper

Structure feature extraction algorithm for multi-modal forearm registration

Sibo You, Yan Ding, Bo Mo, Weizhong Zhang, Jiaxin Li, Lingxi Guo, Zhe Yang

Proceedings Volume 13157, 131570H (2024) https://doi.org/10.1117/12.3016103

Read Abstract +

While convolutional neural networks have shown promise in medical image registration, their inherent complexity limits their registration speed, particularly for surgical applications. Additionally, traditional feature-based matching methods struggle with multi-modal forearm image registration due to the simplicity of forearm skin textures. To address these issues, we propose a robust forearm feature point extraction method based on the forearm’s structural invariance. We combine this method with thin plate spline interpolation to achieve multi-modal forearm registration. Our approach introduces the Forearm Feature Representation Curve (FFRC) and the Multi-Modal Image Registration Framework (FAM) for aligning forearm images with digital anatomical models. FFRC identifies feature points based on forearm structural characteristics, and FAM employs FFRC for matching point pre-screening before applying an affine transformation. For deformable registration which adds Thin Plate Spline (FAM-TPS) uses the matched points as control points. In our experiments, both FAM and FAM-TPS demonstrate high registration accuracy, with FAM-TPS outperforming conventional feature-based methods. Our framework excels at registering forearm images with varying rotation angles, and we have observed a strong correlation between the feature curve’s peak value and the rotation angle. These results affirm the effectiveness of our approach in achieving precise and resilient registration.

Proceedings Article | 27 March 2022 Paper

DEDNet: an infrared and visible image fusion architecture based on dual-encoder and channel-picking/Gaussian-filtering models

Haitan Li, Yan Ding, Xinliang Huang, Weidong Liang, Lingxi Guo

Proceedings Volume 12169, 1216952 (2022) https://doi.org/10.1117/12.2624297

KEYWORDS: Image fusion, Infrared radiation, Infrared imaging, Visible radiation, Image quality

Read Abstract +

DenseFuse is a new approach for infrared and visible image fusion. Considering the single encoding strategy of DenseFuse, we propose a dual-encoder DenseNet (DEDNet), which develops a heterogenous image dual-encoder and a channel picking/Gaussian filtering based fusion strategy. The proposed method includes encoding layers, fusion strategy and decoder, in which encoding layers consist of dual encoders. Since infrared and visible images have different imaging mechanisms, the dual encoders can extract the features of infrared and visible images more effectively and improve the quality of fused images. The fusion strategy based on l1-norm's channel selection and Gaussian filtering improves the structural integrity and spatial correlation of the fused features. In the DEDNet, the infrared image is input to the infrared encoder to get the infrared features while the visible image is input to the visible encoder to get the visible features. Then, the fusion strategy fuses the infrared and visible features structurally and spatially. Finally, the decoder reconstructs the fused features to obtain the fused image. Experiments show that the DEDNet achieves competitive results in both subjective and objective evaluation metrics compared with other fusion approaches.

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