Ms. Defeng Zhang Profile

Defeng Zhang

at Soochow Univ

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 | 2 March 2018 Paper

Automatic localization and segmentation of optical disk based on faster R-CNN and level set in fundus image

Defeng Zhang, Weifang Zhu, Heming Zhao, Fei Shi, Xinjian Chen

Proceedings Volume 10574, 105741U (2018) https://doi.org/10.1117/12.2292950

KEYWORDS: Image segmentation, Blood vessels, Image processing algorithms and systems, Optical discs, Databases, Image quality, Image processing, Image enhancement, Digital filtering, Nonlinear filtering

Read Abstract +

The processing and analysis of retinal fundus images is widely studied because many ocular fundus diseases such as diabetic retinopathy, hypertensive retinopathy, etc., can be diagnosed and treated based on the corresponding analysis results. The optic disc (OD), as the main anatomical structure of ocular fundus, its shape, border, size and pathological depression are very important auxiliary parameters for the diagnosis of fundus diseases. So the precise localization and segmentation of OD is important. Considering the excellent performance of deep learning in object detection and location, an automatic OD localization and segmentation algorithm based on Faster R-CNN and shape constrained level set is presented in this paper. First, Faster R-CNN+ZF model is used to locate the OD via a bounding box (B-box). Second, the main blood vessels in the B-box are removed by Hessian matrix if necessary. Finally, a shape constrained level set algorithm is used to segment the boundary of the OD. The localization algorithm was trained on 4000 images selected from Kaggle and tested on the MESSIDOR database. For the OD localization, the mean average precision (mAP) of 99.9% was achieved, with average time of 0.21s per image. The segmentation algorithm was tested on 120 images randomly selected from MESSIDOR database, achieving an average matching score of 85.4%.

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