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Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images

Authors : Mohamed Elawady, Ibrahim Sadek, Abd El Rahman Shabayek, Gerard  Pons, Sergi Ganau

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

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.

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Footnotes
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Metadata
Title
Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images
Authors
Mohamed Elawady
Ibrahim Sadek
Abd El Rahman Shabayek
Gerard  Pons
Sergi Ganau
Copyright Year
2016
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-41500-0

Electronic ISBN: 978-3-319-41501-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-41501-7_24

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