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Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

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Abstract

Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: pre-processing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice.

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Funding

This work is partially supported by grants from the National Natural Science Foundation of China (NSFC: 61401451, 61501444, 61472411), Guangdong Province Science and Technology Plan Projects (Grant No. 2015B020233004), and Shenzhen Technology Research Project (Grant No. JSGG20160429192140681, JCYJ20160429174611494).

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Author notes

  1. Lei Liu, Kai Li and Wenjian Qin contributed equally to this work.

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People’s Republic of China

    Lei Liu, Wenjian Qin, Tiexiang Wen, Ling Li & Jia Gu

  2. Department of Medical Ultrasonics, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, People’s Republic of China

    Kai Li

  3. University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Wenjian Qin

  4. Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA

    Jia Wu

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  1. Lei Liu
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Correspondence to Jia Wu or Jia Gu.

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Liu, L., Li, K., Qin, W. et al. Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images. Med Biol Eng Comput 56, 183–199 (2018). https://doi.org/10.1007/s11517-017-1770-3

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