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

Breast Tumor Classification of Ultrasound Images Using Wavelet-Based Channel Energy and ImageJ

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

6 Author(s)
Hsieh-Wei Lee ; Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan ; Bin-Da Liu ; King-Chu Hung ; Lei, S.-F.
more authors

The infiltrative nature of lesions is a significant feature that implies a malignant breast lesion in ultrasound images. Characterizing the infiltrative nature of lesions with computationally inexpensive and highly efficacious features is crucial for the realization of a computer-aided diagnosis system. In this study, the infiltrative nature of lesions is regarded as an energy that produces irregular and considerably local variances in a 1-D signal. The local variances can be characterized by a few high octave energies (i.e., the channel energies close to low frequency bands) in 1-D discrete periodized wavelet transform (DPWT). To reduce computation cost, high octave decomposition is performed by a reversible round-off 1-D nonrecursive DPWT (1-D RRO-NRDPWT). A test dataset of breast sonograms with the lesion contour delineated by an experienced physician and three datasets of breast sonograms with the lesion contour delineated by a Java-based image processing program, ImageJ, are built for feature efficacy evaluation. Evaluation with the receiver operating characteristic (ROC) parameters, the area under ROC curve Az, accuracy Ac, sensitivity Se, specificity (Sp), and positive (ppv) and negative predictive values (npv), shows that the proposed feature has an individual performance of (Az, Ac, Se, Sp, ppv, npv) = (0.991, 0.951, 0.985, 0.933, 0.973, 0.992) and (0.934, 0.844, 0.933, 0.795, 0.714, 0.956) for manual and ImageJ-generated datasets, respectively. The performance differences in the three ImageJ-generated datasets derived by variant setting parameters are not significant. Experimental results also reveal that the proposed feature is suitable for combination with some morphometric parameters for performance improvement.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:3 ,  Issue: 1 )

Date of Publication:

Feb. 2009

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.