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Breast ultrasound image segmentation: a survey

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation.

Methods

In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly.

Results

We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity

Conclusions

To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.

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Funding

This work was partially funded by National Natural Science Foundation of China (Nos. 61271314, 61372007, 61401286, and 61571193) and Guangdong Provincial Science and Technology Program—International Collaborative Projects (No. 2014A050503020).

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Authors and Affiliations

  1. School of Electronics and Information, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Qinghua Huang

  2. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Qinghua Huang

  3. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Qinghua Huang, Yaozhong Luo & Qiangzhi Zhang

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  1. Qinghua Huang
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  2. Yaozhong Luo
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  3. Qiangzhi Zhang
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Correspondence to Qinghua Huang.

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Huang, Q., Luo, Y. & Zhang, Q. Breast ultrasound image segmentation: a survey. Int J CARS 12, 493–507 (2017). https://doi.org/10.1007/s11548-016-1513-1

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