Bilateral image subtraction features for multivariate automated classification of breast cancer risk

Jose M. Celaya-Padilla; Juan Rodriguez-Rojas; Jorge I. Galván-Tejada; Antonio Martínez-Torteya; Victor Treviño; José G. Tamez-Peña

doi:10.1117/12.2043870

24 March 2014 Bilateral image subtraction features for multivariate automated classification of breast cancer risk

Jose M. Celaya-Padilla, Juan Rodriguez-Rojas, Jorge I. Galván-Tejada, Antonio Martínez-Torteya, Victor Treviño, José G. Tamez-Peña

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90351T (2014) https://doi.org/10.1117/12.2043870
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Early tumor detection is key in reducing breast cancer deaths and screening mammography is the most widely available method for early detection. However, mammogram interpretation is based on human radiologist, whose radiological skills, experience and workload makes radiological interpretation inconsistent. In an attempt to make mammographic interpretation more consistent, computer aided diagnosis (CADx) systems has been introduced. This paper presents an CADx system aimed to automatically triage normal mammograms form suspicious mammograms. The CADx system co-reregister the left and breast images, then extracts image features from the co-registered mammographic bilateral sets. Finally, an optimal logistic multivariate model is generated by means of an evolutionary search engine. In this study, 440 subjects form the DDSM public data sets were used: 44 normal mammograms, 201 malignant mass mammograms, and 195 mammograms with malignant calci cations. The results showed a cross validation accuracy of 0.88 and an area under receiver operating characteristic (AUC) of 0.89 for the calci cations vs. normal mammograms. The optimal mass vs. normal mammograms model obtained an accuracy of 0.85 and an AUC of 0.88.

Citation Download Citation

Jose M. Celaya-Padilla, Juan Rodriguez-Rojas, Jorge I. Galván-Tejada, Antonio Martínez-Torteya, Victor Treviño, and José G. Tamez-Peña "Bilateral image subtraction features for multivariate automated classification of breast cancer risk", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351T (24 March 2014); https://doi.org/10.1117/12.2043870

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available