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Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow

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Abstract

Purpose

As a key technology in high-intensity focused ultrasound (HIFU) ablation systems, a precise ultrasound image segmentation method for tumor boundary detection is helpful for ablation of tumors and avoiding tumor recurrence. This study explores a new deformable snake model called multi-scale generalized gradient vector flow (MS-GGVF) to segment ultrasound images in HIFU ablation.

Methods

The main idea of the technique is dealing with two issues including spurious boundary attenuation and setting the standard deviation of the Gaussian filter. We assign the standard deviation as scales to build the MS-GGVF model and create a signed distance map to use its gradient direction information and magnitude information to refine the multi-scale edge map by attenuating spurious boundaries and highlighting the real boundary. In addition, a fast generalized gradient vector flow computation algorithm based on an augmented Lagrangian method is introduced to calculate the external force vector field to improve the computation efficiency of our model.

Results

The experimental segmentations were similar to the ground truths delineated by two medical physicians with high area overlap measure and low mean contour distance.

Conclusion

The experimental results demonstrate that the proposed algorithm is robust, reliable, and precise for tumor boundary detection in HIFU ablation systems.

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Acknowledgments

The authors would like to thank Wang and his team from Chongqing University of Medical Sciences for providing the medical background as well as the reviewers for their comments. National Basic Research Program 973 of China (Grant No. 2011CB707904).

Conflict of interest

None of the authors have any conflicts of interest.

Ethical standard

This article does not contain any studies with human or animal subjects performed by any of the authors.

Author information

Authors and Affiliations

  1. Electronic Information School, Wuhan University, 16 Luojiashan Road, Wuchang District, Wuhan, 430079, China

    Yi Le, Xianze Xu & Wencheng Zhao

  2. Ultrasonic Department, Hubei Cancer Hospital, 116 Zhuodaoquan South Road, Wuchang District, Wuhan, 430079, China

    Li Zha & Yanyan Zhu

Authors
  1. Yi Le
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  2. Xianze Xu
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  3. Li Zha
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  4. Wencheng Zhao
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  5. Yanyan Zhu
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Corresponding author

Correspondence to Xianze Xu.

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Cite this article

Le, Y., Xu, X., Zha, L. et al. Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow. J Med Ultrasonics 42, 25–38 (2015). https://doi.org/10.1007/s10396-014-0559-3

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  • DOI: https://doi.org/10.1007/s10396-014-0559-3

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