title: Breast ultrasound image segmentation: a survey.
abstract: OBJECTIVE
  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.
authors.affiliation: 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. qhhuang@scut.edu.cn.|College of Information Engineering, Shenzhen University, Shenzhen, 518060, China. qhhuang@scut.edu.cn.|School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China. qhhuang@scut.edu.cn.
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Huang
authors.affiliation: School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.
authors.forename: Yaozhong
authors.initials: Y
authors.lastname: Luo
authors.affiliation: School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.
authors.forename: Qiangzhi
authors.initials: Q
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001940:Breast / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio; D014463:Ultrasonography; D016217:Ultrasonography, Mammary / Q000379:methods*
keywords: Breast cancer; Computer-aided diagnosis; Segmentation; Ultrasound