Classification of breast lesions in automated 3D breast ultrasound

Tao Tan; Henkjan Huisman; Bram Platel; Andre Grivegnee; Roel Mus; Nico Karssemeijer

doi:10.1117/12.877924

4 March 2011 Classification of breast lesions in automated 3D breast ultrasound

Tao Tan, Henkjan Huisman, Bram Platel, Andre Grivegnee, Roel Mus, Nico Karssemeijer

Author Affiliations +

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79630X (2011) https://doi.org/10.1117/12.877924
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

In this paper we investigated classification of malignant and benign lesions in automated 3D breast ultrasound (ABUS). As a new imaging modality, ABUS overcomes the drawbacks of 2D hand-held ultrasound (US) such as its operator dependence and limited capability in visualizing the breast in 3D. The classification method we present includes a 3D lesion segmentation stage based on dynamic programming, which effectively deals with limited visibility of lesion boundaries due to shadowing and speckle. A novel aspect of ABUS imaging, in which the breast is compressed by means of a dedicated membrane, is the presence of spiculation in coronal planes perpendicular to the transducer. Spiculation patterns, or architectural distortion, are characteristic for malignant lesions. Therefore, we compute a spiculation measure in coronal planes and combine this with more traditional US features related to lesion shape, margin, posterior acoustic behavior, and echo pattern. However, in our work the latter features are defined in 3D. Classification experiments were performed with a dataset of 40 lesions including 20 cancers. Linear discriminant analysis (LDA) was used in combination with leaveone- patient-out and feature selection in each training cycle. We found that spiculation and margin contrast were the most discriminative features and that these features were most often chosen during feature selection. An Az value of 0.86 was obtained by merging all features, while an Az value of 0.91 was obtained by feature selection.

Citation Download Citation

Tao Tan, Henkjan Huisman, Bram Platel, Andre Grivegnee, Roel Mus, and Nico Karssemeijer "Classification of breast lesions in automated 3D breast ultrasound", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630X (4 March 2011); https://doi.org/10.1117/12.877924

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