Mr. Ligong Han Profile

Ligong Han

at Carnegie Mellon 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 | 13 March 2017 Presentation + Paper

An automatic cells detection and segmentation

Ligong Han, T. Hoang Ngan Le, Marios Savvides

Proceedings Volume 10137, 101370W (2017) https://doi.org/10.1117/12.2254068

KEYWORDS: Neodymium, Blood, Image segmentation, RGB color model, Detection and tracking algorithms, Principal component analysis, Image filtering, Visualization, Pathology, CMYK color model

Read Abstract +

This paper presents an end-to-end framework for automatically detecting and segmenting blood cells including normal red blood cells (RBCs), connected RBCs, abnormal RBCs (i.e. tear drop, burr cell, helmet, etc.) and white blood cells (WBCs). Our proposed system contains several components to solve different problems regarding RBCs and WBCs. We first design a novel blood cell color representation which is able to emphasize the RBCs and WBCs in separate channels. Template matching technique is then employed to individually detect RBCs and WBCs in our proposed representation. In order to automatically segment the RBCs and nuclei from WBCs, we develop an adaptive level set-based segmentation method which makes use of both local and global information. The detected and segmented RBCs, however, can be a single RBC, a connected RBC or an abnormal RBC. Therefore, we first separate and reconstruct RBCs from the connected RBCs by our suggested modified template matching. Shape matching by inner distance is later used to classify the abnormal RBCs from the normal RBCs. Our proposed method has been tested and evaluated on different images from ALL-IDB,¹⁰ WebPath,²⁴ UPMC,²³ Flicker datasets, and the one used by Mohamed et al.¹⁴ The precision and recall of RBCs detection are 98.43% and 94.99% respectively, whereas those of WBCs detection are 99.12% and 99.12%. The F-measure of our proposed WBCs segmentation gets up to 95.8%.

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