Breast ultrasound image segmentation: a survey

Qinghua Huang, Yaozhong Luo, Qiangzhi Zhang

科研成果: 期刊稿件文献综述同行评审

188 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)493-507
页数15
期刊International Journal of Computer Assisted Radiology and Surgery
12
3
DOI
出版状态已出版 - 1 3月 2017

指纹

探究 'Breast ultrasound image segmentation: a survey' 的科研主题。它们共同构成独一无二的指纹。

引用此