Mr. Ting Chen Profile

Ting Chen

at Univ of Pennsylvania

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 | 15 May 2003 Paper

Hybrid segmentation framework for 3D medical image analysis

Ting Chen, Dimitri Metaxas

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480116

KEYWORDS: Image segmentation, 3D modeling, Medical imaging, 3D image processing, Image processing, Visual process modeling, Brain, Binary data, Machine vision, Tumors

Read Abstract +

Medical image segmentation is the process that defines the region of interest in the image volume. Classical segmentation methods such as region-based methods and boundary-based methods cannot make full use of the information provided by the image. In this paper we proposed a general hybrid framework for 3D medical image segmentation purposes. In our approach we combine the Gibbs Prior model, and the deformable model. First, Gibbs Prior models are applied onto each slice in a 3D medical image volume and the segmentation results are combined to a 3D binary masks of the object. Then we create a deformable mesh based on this 3D binary mask. The deformable model will be lead to the edge features in the volume with the help of image derived external forces. The deformable model segmentation result can be used to update the parameters for Gibbs Prior models. These methods will then work recursively to reach a global segmentation solution. The hybrid segmentation framework has been applied to images with the objective of lung, heart, colon, jaw, tumor, and brain. The experimental data includes MRI (T1, T2, PD), CT, X-ray, Ultra-Sound images. High quality results are achieved with relatively efficient time cost. We also did validation work using expert manual segmentation as the ground truth. The result shows that the hybrid segmentation may have further clinical use.

Proceedings Article | 1 August 2001 Paper

Embedding watermarks into both DC and AC components of DCT

Yujin Zhang, Ting Chen, Juan Li

Proceedings Volume 4314, (2001) https://doi.org/10.1117/12.435426

KEYWORDS: Digital watermarking, Image filtering, Image compression, Linear filtering, Signal processing, Image processing, Image analysis, Signal detection, Visual system, Electronic filtering

Read Abstract +

The invisibility, robustness, and capacity of watermarks are critical for data authentication. This paper proposes a watermarking scheme taking advantage of both AC and DC coefficients of DCT, to increase the robustness of watermarks in resisting different attacks caused by processing or degradation. In this scheme, watermark components are inserted into DC coefficient and some lower frequency AC coefficients. This enlarges the capacity of watermarks embedded and at the same time keeps the embedded watermarks invisible. The algorithms for embedding both meaningful and meaningless watermarks and detecting these watermarks are described in detail. The image is first divided into blocks and each block is classified into 3 different categories according to its light and texture characteristics. The embedded watermarks are then distributed into both AC and DC coefficients to get a suitable compromise between invisibility and robustness. Some experimental results with real images are presented and they demonstrate that the watermarks generated with the proposed algorithms are more robust against noise and commonly used image-processing techniques than the watermarks generated by using only DC or AC coefficients. The capacity enabled by the proposed algorithms is also bigger than that allowed by the algorithms using no DC coefficients.

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