Deformation robust texture features for polyp classification via CT colonography

Weiguo Cao; Marc J. Pomeroy; Shu Zhang; Perry J. Pickhardt; Hongbing Lu; Zhengrong Liang

doi:10.1117/12.2550404

16 March 2020 Deformation robust texture features for polyp classification via CT colonography

Weiguo Cao, Marc J. Pomeroy, Shu Zhang, Perry J. Pickhardt, Hongbing Lu, Zhengrong Liang

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143E (2020) https://doi.org/10.1117/12.2550404
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

In this article, we introduce a deformation independent model to solve the shape and posture changing issue for polyp characterization in computer-aided diagnosis (CADx) via CT colonography. After volumetric data parameterization in a four-dimensional space, the first fundamental form (FFF) is employed to construct the polyp model which contains several excellent properties such as locality, symmetry, orientation robustness, shift and isometric invariance. In consideration of the scaling effects, gray level co-occurrence matrix (GLCM) is utilized to remove the scaling factor and extract texture descriptors. As a symmetrical square tensor, however, it is difficult to put the FFF into GLCM directly. To solve this problem, we perform matrix decomposition on FFF to extract its eigenvalues and eigenvectors which are used to construct three metric images as the input of GLCM. Then Haralick measures extracted from GLCM are applied to construct texture descriptors which are fed to a random forest classifier to perform polyp classification. Experiments show that the proposed method obtains an encouraging classification performance with area under the curve of receiver operating characteristics (AUC score) of 95.3% which is a significant improvement comparing with five existing methods.

Citation Download Citation

Weiguo Cao, Marc J. Pomeroy, Shu Zhang, Perry J. Pickhardt, Hongbing Lu, and Zhengrong Liang "Deformation robust texture features for polyp classification via CT colonography", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143E (16 March 2020); https://doi.org/10.1117/12.2550404

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Fused deposition modeling

Image classification

Virtual colonoscopy

Feature selection

Tissues

3D modeling

Cancer

Show All Keywords

Keywords/Phrases

Search In:

Publication Years