24 March 2016 First and second-order features for detection of masses in digital breast tomosynthesis
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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.
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Ravi K. Samala, Ravi K. Samala, Jun Wei, Jun Wei, Heang-Ping Chan, Heang-Ping Chan, Lubomir Hadjiiski, Lubomir Hadjiiski, Kenny Cha, Kenny Cha, Mark A. Helvie, 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); doi: 10.1117/12.2216327; https://doi.org/10.1117/12.2216327
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