Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images

TitleAutomatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images
Publication TypeBook Chapter
Year of Publication2016
AuthorsElawady, M., I. Sadek, A. El Rahman Shabayek, G. Pons, and S. Ganau
Book TitleImage Analysis and Recognition
Series Title13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings
ChapterAutomatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images
Series Volume9730
PublisherSpringer International Publishing
CityPóvoa de Varzim, Portugal
ISBN Number978-3-319-41501-7
KeywordsBreast Cancer, Lesion segmentation, Nonlinear filtering, Speckle noise removal, Ultrasound imaging

Breast cancer is one of the leading causes of cancer death among women worldwide. The proposed approach comprises three steps as follows. Firstly, the image is preprocessed to remove speckle noise while preserving important features of the image. Three methods are investigated, i.e., Frost Filter, Detail Preserving Anisotropic Diffusion, and Probabilistic Patch-Based Filter. Secondly, Normalized Cut or Quick Shift is used to provide an initial segmentation map for breast lesions. Thirdly, a postprocessing step is proposed to select the correct region from a set of candidate regions. This approach is implemented on a dataset containing 20 B-mode ultrasound images, acquired from UDIAT Diagnostic Center of Sabadell, Spain. The overall system performance is determined against the ground truth images. The best system performance is achieved through the following combinations: Frost Filter with Quick Shift, Detail Preserving Anisotropic Diffusion with Normalized Cut and Probabilistic Patch-Based with Normalized Cut.