Automatic breast ultrasound image segmentation: A survey

被引：178

作者：

Xian, Min ^{[1
]}

Zhang, Yingtao ^{[3
]}

Cheng, H. D. ^{[2
,3
]}

Xu, Fei ^{[2
]}

Zhang, Boyu ^{[2
]}

Ding, Jianrui ^{[3
]}

机构：

[1] Univ Idaho, Dept Comp Sci, Idaho Falls, ID 83402 USA

[2] Utah State Univ, Dept Comp Sci, Logan, UT 84322 USA

[3] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China

来源：

PATTERN RECOGNITION | 2018年 / 79卷

关键词：

Breast ultrasound (BUS) images; Breast cancer; Segmentation; Benchmark; Early detection; Computer-aided diagnosis (CAD); GEODESIC ACTIVE CONTOURS; GRAPH-BASED SEGMENTATION; LEVEL SET EVOLUTION; DEEP NEURAL-NETWORK; LESION SEGMENTATION; ENERGY MINIMIZATION; TUMOR SEGMENTATION; COMPETITION ALGORITHM; DOMAIN KNOWLEDGE; SNAKE MODEL;

D O I：

10.1016/j.patcog.2018.02.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Many BUS segmentation approaches have been studied in the last two decades, and have been proved to be effective on private datasets. Currently, the advancement of BUS image segmentation seems to meet its bottleneck. The improvement of the performance is increasingly challenging, and only few new approaches were published in the last several years. It is the time to look at the field by reviewing previous approaches comprehensively and to investigate the future directions. In this paper, we study the basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：340 / 355

页数：16

共 178 条

[1] Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion [J].