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A Review of Segmentation Algorithms Applied to B-Mode Breast Ultrasound Images: A Characterization Approach

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Abstract

Ultrasound imaging modality is used prominently for breast cancer screening and diagnosis because of its safety, portability, ease of use and low cost. Over the years, computer-assisted algorithms have been used to aid the radiologists for interpreting the ultrasound images. The presence of speckle adversely affects the ultrasound image quality because of which accurate segmentation of tumors has become a challenging task. In the present work, various machine learning (ML) and deep learning (DL) based approaches designed for segmenting breast ultrasound images have been reviewed over the past two decades using a characterization approach in terms of (a) datasets used, (b) pre-processing methods, (c) augmentation methods, (d) segmentation methods and (e) evaluation metrics used for the segmentation algorithms along with their brainstorming diagrams. The review presents the achievements made till date in the design of ML and DL based segmentation methods applied to breast ultrasound images and also highlights the directions in which the future research could be carried out.

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Acknowledgements

The authors would like to thank Dr. Shruti Thakur, Kamla Nehru Hospital, Shimla for explaining the different sonographic appearances exhibited by different breast tumors. The authors would also like to thank Director, Thapar Institute of Engineering and Technology, Patiala and Director, CSIR-CSIO, Chandigarh for constant patronage and support in carrying out the present research.

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  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Kriti & Ravinder Agarwal

  2. CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, 160030, India

    Jitendra Virmani

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Kriti, Virmani, J. & Agarwal, R. A Review of Segmentation Algorithms Applied to B-Mode Breast Ultrasound Images: A Characterization Approach. Arch Computat Methods Eng 28, 2567–2606 (2021). https://doi.org/10.1007/s11831-020-09469-3

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