NDLI: Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis

Content Provider	IEEE Xplore Digital Library
Author	Padmanabhan, S. Sundararajan, R.
Copyright Year	2012
Description	Author affiliation: Purdue University, West Lafayette, IN 47907, USA (Padmanabhan, S.; Sundararajan, R.)
Abstract	About every minute a woman dies out of breast cancer, worldwide. The need for early detection cannot be overstated. Towards this, mammography is a boon for both early detection and screening of breast cancer tumors. It is an imaging system that uses low dose x-rays for examining the breasts, by the electrons reflected from the tissues. The use of screening mammography is associated with the detection of breast cancer at an earlier stage and smaller size, resulting in a reduction in mortality. This study was aimed at enhancing the current accuracy (diagnostic) of digital mammograms using industry standard simulation software tool, MATLAB and the MIAS dataset. The technique involves identification of tumor cells to segment them in terms of different stages of the disease. We consider the process of object detection, recognition and classification of mammograms with the aim of differentiating between normal and abnormal (benign or cancerous) cells. It is reported that dense breasts can make traditional mammograms more difficult to interpret. Although newer digital mammography techniques claim for better detection in dense breast tissues, the availability of such expensive digital mammograms is not widespread. This problem can be minimized by analyzing different breast structures (mammograms) using the MATLAB numerical analysis software for image processing applications. The results indicated up to 91% accuracy, compared to 70% at present. Our proposed solution has proved to be an effective way of detecting breast cancer early in different types of breast tissues.
Starting Page	153
Ending Page	156
File Size	729855
Page Count	4
File Format	PDF
ISBN	9781467323192
e-ISBN	9781467323222
DOI	10.1109/MVIP.2012.6428783
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-14
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Design automation pattern matching feature extraction Breast Mammogram breast cancer mass detection Testing Biomedical imaging
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automatic detection of breast masses in digital mammograms using pattern matching

Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method

Automated detection of masses in digital mammograms based on pyramid

Hierarchical matching for automatic detection of masses in mammograms

A textural approach for recognizing architectural distortion in mammograms

Circumscribed Mass Detection in Digital Mammograms

A Random Feature Selection Method for Classification of Mammogram Images

Wavelet-based fractal feature extraction for microcalcification detection in mammograms

Mass Detection and Classification on Digitalized Mammograms

Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis

Similar Documents

Automatic detection of breast masses in digital mammograms using pattern matching

Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method

Automated detection of masses in digital mammograms based on pyramid

Hierarchical matching for automatic detection of masses in mammograms

A textural approach for recognizing architectural distortion in mammograms

Circumscribed Mass Detection in Digital Mammograms

A Random Feature Selection Method for Classification of Mammogram Images

Wavelet-based fractal feature extraction for microcalcification detection in mammograms

Mass Detection and Classification on Digitalized Mammograms

Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis