TY - GEN
T1 - Automatic nonlinear filtering and segmentation for breast ultrasound images
AU - Elawady, Mohamed
AU - Sadek, Ibrahim
AU - Shabayek, Abd El Rahman
AU - Pons, Gerard
AU - Ganau, Sergi
N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.
Elawady, M., Sadek, I., Shabayek, A.E.R., Pons, G., Ganau, S. (2016). Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_24
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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.
AB - 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.
KW - breast cancer
KW - lesion segmentation
KW - nonlinear filtering
KW - speckle noise removal
KW - ultrasound imaging
UR - http://www.scopus.com/inward/record.url?scp=84978909336&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-41501-7_24
DO - 10.1007/978-3-319-41501-7_24
M3 - Conference contribution book
AN - SCOPUS:84978909336
SN - 9783319415000
VL - 9730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 213
BT - Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings
A2 - Campilho, Aurelio
A2 - Campilho, Aurelio
A2 - Karray, Fakhri
PB - Springer-Verlag
T2 - 13th International Conference on Image Analysis and Recognition, ICIAR 2016
Y2 - 13 July 2016 through 16 July 2016
ER -
