Abstract:
: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.
journal_name
Biomed Res Intjournal_title
BioMed research internationalauthors
Wu S,Yu S,Zhuang L,Wei X,Sak M,Duric N,Hu J,Xie Ydoi
10.1155/2017/2059036subject
Has Abstractpub_date
2017-01-01 00:00:00pages
2059036eissn
2314-6133issn
2314-6141journal_volume
2017pub_type
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journal_title:BioMed research international
pub_type: 杂志文章
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journal_title:BioMed research international
pub_type: 杂志文章,meta分析
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doi:10.1155/2015/613247
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doi:10.1155/2014/427254
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doi:10.1155/2014/563463
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doi:10.1155/2013/547902
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pub_type: 临床试验,杂志文章
doi:10.1155/2013/783698
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pub_type: 杂志文章
doi:10.1155/2014/201717
更新日期:2014-01-01 00:00:00
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doi:10.1155/2015/613910
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doi:10.1155/2014/639896
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pub_type: 杂志文章,随机对照试验
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