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

Investigation on mammographic image compression and analysis using multiwavelets and neural network

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

2 Author(s)
Ragupathy, U.S. ; Dept. of EIE, Kongu Eng. Coll., Perundurai, India ; Kumar, A.S.

In digital mammography, the resulting electronic image is very large in size. Hence, the size poses a big challenge to the transmission, storage and manipulation of images. Microcalcification is one of the earliest sign of breast cancer and it appears in small size, low contrast radiopacites in high frequency spectrum of mammographic image. Scalar wavelets excel multiwavelets in terms of Peak Signal - to Noise Ratio (PSNR), but fail to capture high frequency information. Multiwavelet preserves high frequency information. This paper proposes multiwavelet based mammographic image compression, and microcalcification analysis in compressed reconstructed images against original images using multiwavelets and neural networks. For a set of four mammography images, the proposed balanced multiwavelet based compression method achieves an average PSNR of 9.064 dB greater than the existing compression scheme. It also details the classification results obtained through the multiwavelet based scheme in comparison with the existing scalar wavelet based scheme. For a testing sample of 30 images, the proposed classification scheme outperforms the scalar wavelet based classification by sensitivity of 2.23% and specificity of 3.4% for original (uncompressed) images. Also it increases the sensitivity by 2.72% and specificity by 8.4% for compressed reconstructed images. This increase in sensitivity and specificity reveals a better performance of the proposed detection scheme.

Published in:

Biomedical Engineering (ICoBE), 2012 International Conference on

Date of Conference:

27-28 Feb. 2012

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.