Journal ArticleDOI
Automated assessment of breast tissue density in digital mammograms
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TLDR
The development of an automatic breast tissue classification methodology is described, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification.About:
This article is published in Computer Vision and Image Understanding.The article was published on 2010-01-01. It has received 120 citations till now. The article focuses on the topics: Mammography.read more
Citations
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Journal ArticleDOI
Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation
Brad M. Keller,Diane L. Nathan,Yan Wang,Yuanjie Zheng,James C. Gee,Emily F. Conant,Despina Kontos +6 more
TL;DR: A new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics is proposed.
Journal ArticleDOI
Three-Class Mammogram Classification Based on Descriptive CNN Features.
TL;DR: A novel classification technique for large data set of mammograms using a deep learning method that targets a three-class classification study (normal, malignant, and benign cases).
Book ChapterDOI
PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification
TL;DR: The promising results obtained by the proposed CAD design indicate its usefulness to assist radiologists for breast density classification.
Journal ArticleDOI
Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM
Shubhi Sharma,Pritee Khanna +1 more
TL;DR: This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant, and proves the applicability of Zernike moments as a fitting texture descriptor.
Journal ArticleDOI
Breast Density Classification Using Multiple Feature Selection
TL;DR: In this paper breast density classification using feature selection process for different classifiers based on grayscale features of first and second order is proposed and results show the improvement on overall classification by choosing the appropriate method and classifier.
References
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Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI
Nonrigid registration using free-form deformations: application to breast MR images
TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
Journal ArticleDOI
Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis
TL;DR: This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer.
Journal ArticleDOI
Quantitative Classification of Mammographic Densities and Breast Cancer Risk: Results From the Canadian National Breast Screening Study
Norman F. Boyd,J W Byng,R A Jong,E. Fishell,L E Little,Anthony B. Miller,Gina Lockwood,David Tritchler,Martin J. Yaffe +8 more
TL;DR: Increases in the level of breast tissue density as assessed by mammography are associated with increases in risk for breast cancer, and these results show that increases in theLevel of breast cancer risk associated with increasing mammographic density is shown.