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Automatic Segmentation of Ultrasound Tomography Image
Joint Authors
Wu, Shibin
Hu, Jiani
Zhuang, Ling
Wei, Xinhua
Sak, Mark
Duric, Neb
Yu, Shaode
Xie, Yaoqin
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-10
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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 (D), Jaccard (J), 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 (D=0.9275 and J=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.
American Psychological Association (APA)
Wu, Shibin& Yu, Shaode& Zhuang, Ling& Wei, Xinhua& Sak, Mark& Duric, Neb…[et al.]. 2017. Automatic Segmentation of Ultrasound Tomography Image. BioMed Research International،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1134491
Modern Language Association (MLA)
Wu, Shibin…[et al.]. Automatic Segmentation of Ultrasound Tomography Image. BioMed Research International No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1134491
American Medical Association (AMA)
Wu, Shibin& Yu, Shaode& Zhuang, Ling& Wei, Xinhua& Sak, Mark& Duric, Neb…[et al.]. Automatic Segmentation of Ultrasound Tomography Image. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1134491
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134491