DSpace
 

iRepository at Perpustakaan UniMAP >
The Library >
Conference Papers >

Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/20569

Title: Investigation on mammographic image compression and analysis using multiwavelets and neural network
Authors: U. S., Ragupathy
A., Senthil Kumar
???metadata.dc.contributor.url???: ask_rect@yahoo.com
ragupathy.us@gmail.com
Keywords: Image compression;Mammography;Microcalcification;Multiwavelet;Neural network
Issue Date: 27-Feb-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 17-21
Series/Report no.: Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012)
Abstract: 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.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: 978-1-4577-1990-5
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178947
http://hdl.handle.net/123456789/20569
Appears in Collections:Conference Papers

Files in This Item:

File Description SizeFormat
1A4.pdfAccess is limited to UniMAP community1.53 MBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! Perpustakaan Tuanku syed Faizuddin Putra, Kampus Pauh Putra, Universiti Malaysia Perlis, 02600, Arau Perlis
TEL: +604-9885420 | FAX: +604-9885405 | EMAIL: rujukan@unimap.edu.my Feedback