Cart (Loading....) | Create Account
Close category search window
 

Texture analysis of CT images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mir, A.H. ; Centre for Biomed. Eng., Indian Inst. of Technol., Delhi, India ; Hanmandlu, M. ; Tandon, S.N.

The present study has shown some promise in the use of texture for the extraction of diagnostic information from CT images. A number of features are obtained from abdominal CT scans of the liver using the spatial domain statistical texture analysis methods: SGLDM, GLRLM, and GLDM. This study investigated whether (a) the texture could be used to discriminate among the various tissue types that are inaccessible to human perception and, (b) if so, then what are the most useful feature parameters for such an application? The efficacies of the different methods were evaluated from the consistency of the computed values within a class and from their differences with other classes. The study has demonstrated the use of texture for tissue characterization of CT images. In particular, we have been successful in identifying the onset of disease in liver tissue, which can not be recognized even by trained human observers. Three useful features, namely entropy (H), local homogeneity (L) and grey level distribution (GLD), have been found effective for pattern recognition. The performance of these features has been compared on the basis of statistical significance. The results show that, except for L, (Direction 0°) all feature parameters perform equally well and detect early malignancy with a confidence level of above 99%-a finding that will not only help in automation, but more importantly, in early detection of malignancy in the liver

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:14 ,  Issue: 6 )

Date of Publication:

Nov/Dec 1995

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.