Mr. Xuesong Yang Profile

Xuesong Yang

at Institute of Automation

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 | 2 March 2018 Paper

Feature extraction using convolutional neural networks for multi-atlas based image segmentation

Xuesong Yang, Yong Fan

Proceedings Volume 10574, 1057439 (2018) https://doi.org/10.1117/12.2293876

KEYWORDS: Image segmentation, Magnetic resonance imaging, Feature extraction, Image fusion, Pattern recognition, Data modeling, Alzheimer's disease, Convolutional neural networks, Network architectures, Performance modeling

Read Abstract +

Multi-atlas based image segmentation in conjunction with pattern recognition based label fusion strategies has achieved promising performance in a variety of image segmentation problems, including hippocampus segmentation from MR images. The pattern recognition based label fusion consists of image feature extraction and pattern recognition components. Since the feature extraction component plays an important role in the pattern recognition based label fusion, a variety of feature extraction methods have been proposed to extract image features, including texture features and random projection features. However, these feature extraction methods are not adaptive to different segmentation problems. Following the success of convolutional neural networks in image feature extraction, we propose a feature extraction method based on convolutional neural networks for multi-atlas based image segmentation. The proposed method has been validated based on 135 T1 magnetic resonance imaging (MRI) scans and their hippocampus labels provided by the EADC-ADNI harmonized segmentation protocol. We also compared our method with state-of-the-art pattern recognition based MAIS methods, including Local Label Learning and Random Local Binary Patterns. The experimental results have demonstrated that our method could achieve competitive hippocampus segmentation performance over the alternative methods under comparison.

Proceedings Article | 2 March 2018 Paper

Coupled dictionary learning for joint MR image restoration and segmentation

Xuesong Yang, Yong Fan

Proceedings Volume 10574, 105742Z (2018) https://doi.org/10.1117/12.2293043

KEYWORDS: Image segmentation, Image restoration, Image quality, Associative arrays, Magnetic resonance imaging, Alzheimer's disease, Neuroimaging, Medical imaging, Image fusion, Super resolution

Read Abstract +

To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

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