Paper Info
Title
Wavelet-morphology for mass detection in digital mammogram images
Abstract
In this paper, a novel wavelet-morphology method for the detection of mass abnormalities in digital mammograms is presented. The new scheme utilizes the feature extraction capability of the wavelet transform followed by a novel recursive-enhancement morphology algorithm to detect the masses. A morphology-based segmentation algorithm is finally applied to the enhanced image to separate the mass from the normal breast tissues. This technique outlines the shape of the region of interest (mass in mammograms). Tests results have confirmed the efficacy of the technique in automated detection of abnormalities in wavelet based compressed mammograms.
Year
DOI
Venue
2003
10.1117/12.480109
Proceedings of SPIE
Keywords
Field
DocType
region of interest,wavelet transform,feature extraction,wavelet transforms,wavelets
Digital mammography,Mammography,Computer vision,Pattern recognition,Computer science,Segmentation,Feature extraction,Artificial intelligence,Digital mammogram,Region of interest,Wavelet transform,Wavelet
Conference
Volume
ISSN
Citations 
5032
0277-786X
1
PageRank 
References 
Authors
0.40
0
3
Name
Order
Citations
PageRank
Golshah Naghdy1299.36
Yue Li213342.96
Jian Wang310.73