NDLI: Novel Margin Features for Mammographic Mass Classification

Content Provider	IEEE Xplore Digital Library
Author	Bagheri-Khaligh, A. Zarghami, A. Manzuri-Shalmani, M.T.
Copyright Year	2012
Abstract	Computer-Aided Diagnosis (CAD) systems are widely used for detection of various kinds of abnormalities in mammography images. Masses are one type of these abnormalities which are mostly characterized by their margin and shape. For classification of masses proper features are needed to be extracted. However, the number of well-known features for describing margin is much fewer than geometrical, shape, and textural ones. In addition, most of the existing margin features are highly dependent on segmentation accuracy. In this work, new features for describing margin of masses are presented which can handle inaccuracies in segmentation. These features are obtained from a set of waveforms by wavelet analysis among each of them. For each of these waveforms an edge probability distribution is computed. Then, features are extracted from these probability distributions. Although these features are called margin features, they are highly related to the texture of the mass. For experimentation DDSM dataset was used and our simulations show the great performance of these features in classification of masses.
Starting Page	139
Ending Page	144
File Size	685147
Page Count	6
File Format	PDF
ISBN	9781467346511
DOI	10.1109/ICMLA.2012.209
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Accuracy margin Shape feature extraction Feature extraction Probability distribution Mammography Indexes computer-aided diagnosis mass classification Cancer
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

Applying modular classifiers to mammographic mass classification

Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection

Classification of benign and malignant masses in breast mammograms

Mass Classification in Mammography with Morphological Features and Multiple Kernel Learning

Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation

Mammography Feature Analysis and Mass Detection in Breast Cancer Images

Image features extraction for masses classification in mammograms

A novel breast mass diagnosis system based on Zernike moments as shape and density descriptors

Digital Mammogram Tumor Preprocessing Segmentation Feature Extraction and Classification

Novel Margin Features for Mammographic Mass Classification

Similar Documents

Applying modular classifiers to mammographic mass classification

Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection

Classification of benign and malignant masses in breast mammograms

Mass Classification in Mammography with Morphological Features and Multiple Kernel Learning

Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation

Mammography Feature Analysis and Mass Detection in Breast Cancer Images

Image features extraction for masses classification in mammograms

A novel breast mass diagnosis system based on Zernike moments as shape and density descriptors

Digital Mammogram Tumor Preprocessing Segmentation Feature Extraction and Classification

Novel Margin Features for Mammographic Mass Classification