Automatic Segmentation of Ultrasound Tomography Image

	Automatic Segmentation of Ultrasound Tomography Image
	Shibin Wu; Shaode Yu; Ling Zhuang; Xinhua Wei; Mark Sak; Neb Duric; Jiani Hu; Yaoqin Xie
刊名	Biomed Research International
	2017
文献子类	期刊论文
英文摘要	Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images.The performance of AUGC is validated with breast overlapping metrics (Dice coefficient ( ), Jaccard ( ), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy ( = 0.9275 and = 0.8660 and FP = 0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12002]
专题	深圳先进技术研究院_医工所
作者单位	Biomed Research International
推荐引用方式 GB/T 7714	Shibin Wu,Shaode Yu,Ling Zhuang,et al. Automatic Segmentation of Ultrasound Tomography Image[J]. Biomed Research International,2017.
APA	Shibin Wu.,Shaode Yu.,Ling Zhuang.,Xinhua Wei.,Mark Sak.,...&Yaoqin Xie.(2017).Automatic Segmentation of Ultrasound Tomography Image.Biomed Research International.
MLA	Shibin Wu,et al."Automatic Segmentation of Ultrasound Tomography Image".Biomed Research International (2017).