Nonlinear histogram binning for quantitative analysis of lung tissue fibrosis in high-resolution CT data

Vanessa A. Zavaletta; Brian J. Bartholmai M.D.; Richard A. Robb

doi:10.1117/12.710220

10 May 2007 Nonlinear histogram binning for quantitative analysis of lung tissue fibrosis in high-resolution CT data

Vanessa A. Zavaletta, Brian J. Bartholmai M.D., Richard A. Robb

Author Affiliations +

Proceedings Volume 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images; 65111Q (2007) https://doi.org/10.1117/12.710220
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Diffuse lung diseases, such as idiopathic pulmonary fibrosis (IPF), can be characterized and quantified by analysis of volumetric high resolution CT scans of the lungs. These data sets typically have dimensions of 512 x 512 x 400. It is too subjective and labor intensive for a radiologist to analyze each slice and quantify regional abnormalities manually. Thus, computer aided techniques are necessary, particularly texture analysis techniques which classify various lung tissue types. Second and higher order statistics which relate the spatial variation of the intensity values are good discriminatory features for various textures. The intensity values in lung CT scans range between [-1024, 1024]. Calculation of second order statistics on this range is too computationally intensive so the data is typically binned between 16 or 32 gray levels. There are more effective ways of binning the gray level range to improve classification. An optimal and very efficient way to nonlinearly bin the histogram is to use a dynamic programming algorithm. The objective of this paper is to show that nonlinear binning using dynamic programming is computationally efficient and improves the discriminatory power of the second and higher order statistics for more accurate quantification of diffuse lung disease.

Citation Download Citation

Vanessa A. Zavaletta, Brian J. Bartholmai M.D., and Richard A. Robb "Nonlinear histogram binning for quantitative analysis of lung tissue fibrosis in high-resolution CT data", Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65111Q (10 May 2007); https://doi.org/10.1117/12.710220

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