A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

Authors:
Stylianos D. Tzikopoulos;Michael E. Mavroforakis;Harris V. Georgiou;Nikos Dimitropoulos;Sergios Theodoridis
Affiliations:
National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens 15784, Greece;University of Houston, Department of Computer Science, 501 P.G. Hoffman Hall, Houston, TX 77204-3010, USA;National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens 15784, Greece;Delta Digital Imaging, Semitelou 6, Athens 11528, Greece;National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens 15784, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2011

Citing 9
Cited 3

Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Modeling and Classifying Breast Tissue Density in Mammograms

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A novel SVM Geometric Algorithm based on Reduced Convex Hulls

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Pattern Recognition, Fourth Edition

Pattern Recognition, Fourth Edition
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way

A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Automatic classification of breast tissue

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Extended fractal analysis for texture classification and segmentation

IEEE Transactions on Image Processing
A geometric approach to Support Vector Machine (SVM) classification

IEEE Transactions on Neural Networks
A Geometric Nearest Point Algorithm for the Efficient Solution of the SVM Classification Task

IEEE Transactions on Neural Networks

Detection of masses based on asymmetric regions of digital bilateral mammograms using spatial description with variogram and cross-variogram functions

Computers in Biology and Medicine
Enhanced pose normalization and matching of non-rigid objects based on support vector machine modelling

Pattern Recognition
Breast density classification to reduce false positives in CADe systems

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimension-related feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure. The results were evaluated by expert radiologists and are very promising, showing equal or higher success rates compared to other related works, despite the fact that some of them used only selected portions of this specific mammographic database. In contrast, our methodology is applied to the complete miniMIAS database and it exhibits the reliability that is normally required for clinical use in CAD systems.