Dr. Xulei Yang Profile

Dr. Xulei Yang

at A*STAR Institute of High Performance Computing

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 | 23 March 2016 Paper

Color-texture based extreme learning machines for tissue tumor classification

X. Yang, S. Yeo, S. Wong, G. Lee, Y. Su, J. Hong, A. Choo, S. Chen

Proceedings Volume 9791, 97910W (2016) https://doi.org/10.1117/12.2216573

KEYWORDS: Tissues, Image classification, Feature extraction, Computer aided diagnosis and therapy, Computing systems, Pathology, Classification systems, Kidney, Tumors, Cancer, Machine learning, Image processing

Read Abstract +

In histopathological classification and diagnosis of cancer cases, pathologists perform visual assessments of immunohistochemistry (IHC)-stained biomarkers in cells to determine tumor versus non-tumor tissues. One of the prerequisites for such assessments is the correct identification of regions-of-interest (ROIs) with relevant histological features. Advances in image processing and machine learning give rise to the possibility of full automation in ROI identification by identifying image features such as colors and textures. Such computer-aided diagnostic systems could enhance research output and efficiency in identifying the pathology (normal, non-tumor or tumor) of a tissue pattern from ROI images. In this paper, a computational method using color-texture based extreme learning machines (ELM) is proposed for automatic tissue tumor classification. Our approach consists of three steps: (1) ROIs are manually identified and annotated from individual cores of tissue microarrays (TMAs); (2) color and texture features are extracted from the ROIs images; (3) ELM is applied to the extracted features to classify the ROIs into non-tumor or tumor categories. The proposed approach is tested on 100 sets of images from a kidney cancer TMA and the results show that ELM is able to achieve classification accuracies of 91.19% and 88.72% with a Gaussian radial basis function (RBF) and linear kernel, respectively, which is superior to using SVM with the same kernels.

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