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Segmentation Method for ROI Detection in Mammographic Images using Wiener Filter and Kittler�s Method

Published on May 2013 by Pragathi. J, H. T. Patil
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International Conference on Recent Trends in Engineering and Technology 2013
Foundation of Computer Science USA
ICRTET - Number 4
May 2013
Authors: Pragathi. J, H. T. Patil
93eae708-f770-4457-821f-554121ec349f

Pragathi. J, H. T. Patil . Segmentation Method for ROI Detection in Mammographic Images using Wiener Filter and Kittler�s Method. International Conference on Recent Trends in Engineering and Technology 2013. ICRTET, 4 (May 2013), 27-33.

@article{
author = { Pragathi. J, H. T. Patil },
title = { Segmentation Method for ROI Detection in Mammographic Images using Wiener Filter and Kittler�s Method },
journal = { International Conference on Recent Trends in Engineering and Technology 2013 },
issue_date = { May 2013 },
volume = { ICRTET },
number = { 4 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 27-33 },
numpages = 7,
url = { /proceedings/icrtet/number4/11788-1346/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Engineering and Technology 2013
%A Pragathi. J
%A H. T. Patil
%T Segmentation Method for ROI Detection in Mammographic Images using Wiener Filter and Kittler�s Method
%J International Conference on Recent Trends in Engineering and Technology 2013
%@ 0975-8887
%V ICRTET
%N 4
%P 27-33
%D 2013
%I International Journal of Computer Applications
Abstract

Breast cancer is the second leading cause of death for women all over the world. Screening mammography is currently the best available radiological technique for early detection of breast cancer. However the presence of artifacts can disturb the detection of breast cancer and reduce the rate of accuracy in the computer aided detection (CAD) systems. For this reason, the pre-processing of mammogram images is very important in the process of breast cancer analysis because it reduces the number of false positive. This paper proposes various filtering techniques to solve the noise removal problems and separate the background region from the breast profile region using an automatic thresholding technique and Connected Component Labelling. We evaluated our pre-processing method on a set of images obtained from a private hospital. Thus this preparation phase improves the image quality and accentuates the CAD results more accurate.

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Index Terms

Computer Science
Information Sciences

Keywords

Mammogram Thresholding Connected Component Labelling Breast Region Extraction Computer Aided Analysis Otsu Method Kittler Method Wiener Filter