Automatic Segmentation of Ultrasound Tomography Image.

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 Int

authors

Wu S,Yu S,Zhuang L,Wei X,Sak M,Duric N,Hu J,Xie Y

doi

10.1155/2017/2059036

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

2059036

eissn

2314-6133

issn

2314-6141

journal_volume

2017

pub_type

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