A statistical description of 3D lung texture from CT data

Kraisorn Chaisaowong; Andreas Paul

doi:10.1117/12.2179457

4 March 2015 A statistical description of 3D lung texture from CT data

Kraisorn Chaisaowong, Andreas Paul

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94432F (2015) https://doi.org/10.1117/12.2179457
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

A method was described to create a statistical description of 3D lung texture from CT data. The second order statistics, i.e. the gray level co-occurrence matrix (GLCM), has been applied to characterize texture of lung by defining the joint probability distribution of pixel pairs. The required GLCM was extended to three-dimensional image regions to deal with CT volume data. For a fine-scale lung segmentation, both the 3D GLCM of lung and thorax without lung are required. Once the co-occurrence densities are measured, the 3D models of the joint probability density function for each describing direction of involving voxel pairs and for each class (lung or thorax) are estimated using mixture of Gaussians through the expectation-maximization algorithm. This leads to a feature space that describes the 3D lung texture.

Citation Download Citation

Kraisorn Chaisaowong and Andreas Paul "A statistical description of 3D lung texture from CT data", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94432F (4 March 2015); https://doi.org/10.1117/12.2179457

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