Abstract
Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a complementary diagnostic method for early detection of breast cancer. However, due to the large amount of information, DCE-MRI data can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of segmented regions of interest according to their aggressiveness.
While there is a certain amount of evidence that dynamic information can be suitably used for lesion classification, it still remains unclear whether other kinds of features (e.g. texture-based) can add useful information. This pushes the exploration of new features coming from different research fields such as Local Binary Pattern (LBP) and its variants. In particular, in this work we propose to use LBP-TOP (Three Orthogonal Projections) for the assessment of lesion malignancy in breast DCE-MRI. Different classifiers as well as the influence of a motion correction technique have been considered. Our results indicate an improvement by using LPB-TOP in combination with a Random Forest classifier (84.6% accuracy) with respect to previous findings in literature.
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Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., Sansone, C. (2015). LBP-TOP for Volume Lesion Classification in Breast DCE-MRI. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_58
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DOI: https://doi.org/10.1007/978-3-319-23231-7_58
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