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

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Main Authors: U. S., Ragupathy, A., Senthil Kumar
Other Authors: ask_rect@yahoo.com
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Image compression
Mammography
Microcalcification
Multiwavelet
Neural network
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20569
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spelling my.unimap-205692012-08-03T05:45:53Z Investigation on mammographic image compression and analysis using multiwavelets and neural network U. S., Ragupathy A., Senthil Kumar ask_rect@yahoo.com ragupathy.us@gmail.com Image compression Mammography Microcalcification Multiwavelet Neural network Link to publisher's homepage at http://ieeexplore.ieee.org/ 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. 2012-08-03T05:45:53Z 2012-08-03T05:45:53Z 2012-02-27 Working Paper p. 17-21 978-1-4577-1990-5 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178947 http://hdl.handle.net/123456789/20569 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Image compression
Mammography
Microcalcification
Multiwavelet
Neural network
spellingShingle Image compression
Mammography
Microcalcification
Multiwavelet
Neural network
U. S., Ragupathy
A., Senthil Kumar
Investigation on mammographic image compression and analysis using multiwavelets and neural network
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 ask_rect@yahoo.com
author_facet ask_rect@yahoo.com
U. S., Ragupathy
A., Senthil Kumar
format Working Paper
author U. S., Ragupathy
A., Senthil Kumar
author_sort U. S., Ragupathy
title Investigation on mammographic image compression and analysis using multiwavelets and neural network
title_short Investigation on mammographic image compression and analysis using multiwavelets and neural network
title_full Investigation on mammographic image compression and analysis using multiwavelets and neural network
title_fullStr Investigation on mammographic image compression and analysis using multiwavelets and neural network
title_full_unstemmed Investigation on mammographic image compression and analysis using multiwavelets and neural network
title_sort investigation on mammographic image compression and analysis using multiwavelets and neural network
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20569
_version_ 1643793116518416384
score 13.222552

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