Classification of breast masses and normal tissues in digital tomosynthesis mammography

17 March 2008 Classification of breast masses and normal tissues in digital tomosynthesis mammography

Jun Wei, Heang-Ping Chan, Yiheng Zhang, Berkman Sahiner, Chuan Zhou, Jun Ge, Yi-Ta Wu, Lubomir M. Hadjiiski

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 691508 (2008) https://doi.org/10.1117/12.771189
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Digital tomosynthesis mammography (DTM) can provide quasi-3D structural information of the breast by reconstructing the breast volume from projection views (PV) acquired in a limited angular range. Our purpose is to design an effective classifier to distinguish breast masses from normal tissues in DTMs. A data set of 100 DTM cases collected with a GE first generation prototype DTM system at the Massachusetts General Hospital was used. We reconstructed the DTMs using a simultaneous algebraic reconstruction technique (SART). Mass candidates were identified by 3D gradient field analysis. Three approaches to distinguish breast masses from normal tissues were evaluated. In the 3D approach, we extracted morphological and run-length statistics texture features from DTM slices as input to a linear discriminant analysis (LDA) classifier. In the 2D approach, the raw input PVs were first preprocessed with a Laplacian pyramid multi-resolution enhancement scheme. A mass candidate was then forward-projected to the preprocessed PVs in order to determine the corresponding regions of interest (ROIs). Spatial gray-level dependence (SGLD) texture features were extracted from each ROI and averaged over 11 PVs. An LDA classifier was designed to distinguish the masses from normal tissues. In the combined approach, the LDA scores from the 3D and 2D approaches were averaged to generate a mass likelihood score for each candidate. The A_z values were 0.87±0.02, 0.86±0.02, and 0.91±0.02 for the 3D, 2D, and combined approaches, respectively. The difference between the A_z values of the 3D and 2D approaches did not achieve statistical significance. The performance of the combined approach was significantly (p<0.05) better than either the 3D or 2D approach alone. The combined classifier will be useful for false-positive reduction in computerized mass detection in DTM.

Citation Download Citation

Jun Wei, Heang-Ping Chan, Yiheng Zhang, Berkman Sahiner, Chuan Zhou, Jun Ge, Yi-Ta Wu, and Lubomir M. Hadjiiski "Classification of breast masses and normal tissues in digital tomosynthesis mammography", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691508 (17 March 2008); https://doi.org/10.1117/12.771189

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Breast

Digital mammography

Tissues

Statistical analysis

Computer aided diagnosis and therapy

Feature extraction

Reconstruction algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years