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Automatic gallbladder and gallstone regions segmentation in ultrasound image

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

As gallbladder diseases including gallstone and cholecystitis are mainly diagnosed by using ultra-sonographic examinations, we propose a novel method to segment the gallbladder and gallstones in ultrasound images.

Methods

The method is divided into five steps. Firstly, a modified Otsu algorithm is combined with the anisotropic diffusion to reduce speckle noise and enhance image contrast. The Otsu algorithm separates distinctly the weak edge regions from the central region of the gallbladder. Secondly, a global morphology filtering algorithm is adopted for acquiring the fine gallbladder region. Thirdly, a parameter-adaptive pulse-coupled neural network (PA-PCNN) is employed to obtain the high-intensity regions including gallstones. Fourthly, a modified region-growing algorithm is used to eliminate physicians’ labeled regions and avoid over-segmentation of gallstones. It also has good self-adaptability within the growth cycle in light of the specified growing and terminating conditions. Fifthly, the smoothing contours of the detected gallbladder and gallstones are obtained by the locally weighted regression smoothing (LOESS).

Results

We test the proposed method on the clinical data from Gansu Provincial Hospital of China and obtain encouraging results. For the gallbladder and gallstones, average similarity percent of contours (EVA) containing metrics dice’s similarity , overlap fraction and overlap value is 86.01 and 79.81%, respectively; position error is 1.7675 and 0.5414 mm, respectively; runtime is 4.2211 and 0.6603 s, respectively. Our method then achieves competitive performance compared with the state-of-the-art methods.

Conclusions

The proposed method is potential to assist physicians for diagnosing the gallbladder disease rapidly and effectively.

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Acknowledgements

The authors thank all the reviewers for their valuable comments, which further improved the quality of the paper. We also thank Gansu Provincial Hospital for providing the dataset. This study was funded National Natural Science Foundation of China (grant numbers 61175012 & 61201422), Natural Science Foundation of Gansu Province of China (grant number 148RJZA044) and Youth Foundation of Lanzhou Jiaotong University of China (grant number 2014005).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Jing Lian, Yide Ma, Yurun Ma, Jizhao Liu, Zhen Yang & Yanan Guo

  2. Equipment Management Department, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China

    Bin Shi

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  1. Jing Lian
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  2. Yide Ma
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Correspondence to Yide Ma.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Lian, J., Ma, Y., Ma, Y. et al. Automatic gallbladder and gallstone regions segmentation in ultrasound image. Int J CARS 12, 553–568 (2017). https://doi.org/10.1007/s11548-016-1515-z

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