DocumentCode :
3073182
Title :
Feature extraction from mammograms by using a´priory information
Author :
Mijovic, Stefan ; Dakovic, Milos
Author_Institution :
Fac. of Natural Sci. & Math., Univ. of Montenegro, Podgorica, Montenegro
fYear :
2013
fDate :
15-20 June 2013
Firstpage :
144
Lastpage :
147
Abstract :
The aim of X-ray imaging is to visualize the structure of body tissue with high spatial resolution and high contrast between different kinds of tissues. Early diagnosis of breast cancer requires almost a perfect imaging system or applying sophisticated post processing algorithms to `clean´ the image and extract needed information. Diagnostic images are often assessed in clinical practice by using subjective methods which are limited by the skill of the radiologist. A numerous computer-aided diagnosis (CAD) algorithms, that assist reviewers in their decisions and reduce the numbers of false positives, have been developed. However, these systems have not been well utilized yet and there are not the consensus among scientists and practitioners which are the most effective. Straightforward deconvolution of the image, using the known imperfectness of the imaging system, is not applicable due to non-avoidable noise. The noise makes these deconvolution procedures ill-posed from a mathematical point of view i.e., the solution is not stable and must be regularized. The level of regularization depends on an a´priory information about the image formation. A regularized deconvolution procedure, based on the knowledge of the noise estimation, is proposed for de-blurring mammograms and thus enhanced the features relevant for clinical outcomes. Testing its limitations is done as well. Promising preliminary results are obtained. Further investigations are needed to apply this procedure to a real mammographic unit.
Keywords :
biological tissues; cancer; data visualisation; deconvolution; feature extraction; image resolution; image restoration; mammography; medical image processing; patient diagnosis; CAD algorithms; X-ray imaging; a-priory information; body tissue structure visualization; breast cancer diagnosis; computer-aided diagnosis algorithms; false positive reduction; feature extraction; image cleaning; image contrast; image deconvolution; image postprocessing algorithms; information extraction; mammogram image deblurring; noise estimation; regularized deconvolution procedure; spatial resolution; Delta-sigma modulation; MATLAB; deconvolution; image processing; mammograms; modulation transfer function; noise; point spread function; regularization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Embedded Computing (MECO), 2013 2nd Mediterranean Conference on
Conference_Location :
Budva
ISSN :
1800-993X
Type :
conf
DOI :
10.1109/MECO.2013.6601341
Filename :
6601341
Link To Document :
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