Dr. Lijie Deng Profile

Dr. Lijie Deng

at Sun Yat-Sen 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 | 12 March 2018 Paper

Automatic segmentation of corneal ulcer area based on ocular staining images

Lijie Deng, Haixiang Huang, Jin Yuan, Xiaoying Tang

Proceedings Volume 10578, 105781D (2018) https://doi.org/10.1117/12.2293270

KEYWORDS: Image segmentation, Cornea, RGB color model, Eye, Surgery

Read Abstract +

In this study, we proposed and validated a novel and accurate automatic approach for ulcer area extraction from ocular staining images. We first segmented the corneal surface area with the help of four pre-defined key landmarks by modeling the corneal surface shape as an ellipse. Then the ulcer area was identified within the cornea by employing a combination of techniques: 1) iterative k-means based clustering to extract areas with similar color information; 2) morphological operations to polish results from the previous step, with the parameters employed in the morphological operators determined automatically via linear regression analysis; 3) region growing to select the true ulcer area among a number of separated areas. To validate this automatic approach, we compared its results with those from manual delineations using the Dice Overlap Score (DSC) and the automatic-versus-manual correlation in terms of the ulcer area size based on 48 ocular staining images with corneal ulcers. The automatic results showed strong and statistically significant positive correlations with the manual ones in terms of both the cornea size and the ulcer area size (cornea: PCC=0.842, p-value=6:890 x 10^-14; corneal ulcer area: PCC=0.969, p-value=1:119 x 10^-29). For cornea, the DSC between the proposed automatic results and the manual ones is on average 0.989, whereas the average DSC for the ulcer segmentation is 0.879. This suggests a high overlap between the automatic and the manual results for both the cornea and the corneal ulcer area. We also compared the proposed method with a classic segmentation approach (the active contour). Our results revealed a superior performance of the proposed automatic approach in corneal ulcer area identification relative to the active contour (0.879 versus 0.639 in terms of DSC).

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