Open Access Research article

Characterizing mammographic images by using generic texture features

Lothar Häberle1, Florian Wagner23, Peter A Fasching14*, Sebastian M Jud1, Katharina Heusinger1, Christian R Loehberg1, Alexander Hein1, Christian M Bayer1, Carolin C Hack1, Michael P Lux1, Katja Binder1, Matthias Elter2, Christian Münzenmayer2, Rüdiger Schulz-Wendtland5, Martina Meier-Meitinger5, Boris R Adamietz5, Michael Uder5, Matthias W Beckmann1 and Thomas Wittenberg2

Author Affiliations

1 University Breast Center for Franconia, Erlangen-Nuremberg Comprehensive Cancer Center, Erlangen University Hospital, Department of Gynecology and Obstetrics, Universitaetsstrasse 21-23, 91054 Erlangen, Germany

2 Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany

3 International Max Planck Research School (IMPRS) for Optics and Imaging, Erlangen, Germany

4 Department of Medicine, Division of Hematology and Oncology, David Geffen School of Medicine, University of California at Los Angeles, USA

5 Institute of Diagnostic Radiology, Erlangen University Hospital, Erlangen-Nuremberg Comprehensive Cancer Center, Universitaetsstrasse 21-23, 91054 Erlangen, Germany

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Breast Cancer Research 2012, 14:R59  doi:10.1186/bcr3163

Published: 10 April 2012

Abstract

Introduction

Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design.

Methods

A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model.

Results

Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model.

Conclusions

Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.