NDLI: A Review on Mammogram Image Enhancement Techniques for Breast Cancer Detection

Content Provider	IEEE Xplore Digital Library
Author	Gowri, D.S. Amudha, T.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Applic., Bharathiar Univ., Coimbatore, India (Gowri, D.S.; Amudha, T.)
Abstract	The aim of this paper is to review the breast cancer diagnosis methods and breast image enhancement techniques to enable early detection of breast cancer. Breast cancer is the second leading cancer disease in the world. Cancer begins in the cells which are the basic building blocks that make up tissue. Sometimes, the process of cell growth goes wrong and new cells form when the body doesn't need them and old or damaged cells do not die as they should. When this occurs, a build up of cells often forms a mass of tissue called a lump, growth, or tumor. One of the challenges faced by the current mammogram image detection techniques lies in the difficulty of analyzing dense tissues. Mammogram image enhancement is the process of manipulating mammogram images to increase their contrast and decrease the noise present in order to aid radiologists in the detection of abnormalities. This paper details upon the breast cancer detection methods and different types of databases where mammogram images could be collected. Finally, it touches upon various processes to enhance the mammogram images. A variety of enhancement techniques are used to provide rich look of mammogram image to detect breast cancer easily.
Starting Page	47
Ending Page	51
File Size	139665
Page Count	5
File Format	PDF
ISBN	9781479939664
DOI	10.1109/ICICA.2014.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Finite impulse response filters Image Enhancement Breast Cancer Diagnostic Methods IIR filters Breast cancer Breast Cancer Mammogram Image Database
Content Type	Text
Resource Type	Article

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