First and second-order features for detection of masses in digital breast tomosynthesis

Ravi K. Samala; Jun Wei; Heang-Ping Chan; Lubomir Hadjiiski; Kenny Cha; Mark A. Helvie

doi:10.1117/12.2216327

24 March 2016 First and second-order features for detection of masses in digital breast tomosynthesis

Ravi K. Samala, Jun Wei, Heang-Ping Chan, Lubomir Hadjiiski, Kenny Cha, Mark A. Helvie

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978523 (2016) https://doi.org/10.1117/12.2216327
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

We are developing novel methods for prescreening of mass candidates in computer-aided detection (CAD) system for digital breast tomosynthesis (DBT). With IRB approval and written informed consent, 186 views from 94 breasts were imaged using a GE GEN2 prototype DBT system. The data set was randomly separated into training and test sets by cases. Gradient field convergence features based on first-order features were used to select the initial set of mass candidates. Eigenvalues based on second-order features from the Hessian matrix were extracted for the mass candidate locations in the DBT volume. The features from the first- and second-order analysis form the feature vector that was input to a linear discriminant analysis (LDA) classifier to generate a candidate-likelihood score. The likelihood scores were ranked and the top N candidates were passed onto the subsequent detection steps. The improvement between using only first-order features and the combination of first and second-order features was analyzed using a rank-sensitivity plot. 3D objects were obtained with two-stage 3D clustering followed by active contour segmentation. Morphological, gradient field, and texture features were extracted and feature selection was performed using stepwise feature selection. A combination of LDA and rule-based classifiers was used for FP reduction. The LDA classifier output a masslikelihood score for each object that was used as a decision variable for FROC analysis. At breast-based sensitivities of 70% and 80%, prescreening using first-order and second-order features resulted in 0.7 and 1.0 FPs/DBT.

Citation Download Citation

Ravi K. Samala, Jun Wei, Heang-Ping Chan, Lubomir Hadjiiski, Kenny Cha, and Mark A. Helvie "First and second-order features for detection of masses in digital breast tomosynthesis", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978523 (24 March 2016); https://doi.org/10.1117/12.2216327

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