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Improved maximally stable extremal regions based method for the segmentation of ultrasonic liver images

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Abstract

The goal of this paper is to propose a modified maximally stable extremal region (MSER) based method for the segmentation of ultrasound liver images. Firstly, the feature regions including liver lesions are extracted using the modified MSER detector. Unlike the MSER algorithm, the improved MSER detector merely needs dozens of gray levels rather than 256 possible gray levels ranging from 0 to 255. Next, the edges of the liver lesions are detected from the binary images, and a merging strategy is designed to refine the contour of the liver lesion. The last step is the segmentation of the liver lesion according to the refined contour. The segmentation results of ultrasound liver images demonstrate that there is a significant correlation between the liver lesions selected by a medical expert and the liver lesions segmented by the proposed method. A comparison of the proposed method and other segmented methods shows that the proposed method can detect a more accurate contour of liver lesion images.

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Acknowledgments

This work was part supported by the National Natural Science Foundation of China under grant No. 61473025, the Fundamental Research Funds for the Central Universities (YS1404), Beijing University of Chemical Technology Interdisciplinary Funds for “Visual Media Computing” and the open-project grant funded by the State Key Laboratory of Synthetical Automation for Process Industry at the Northeastern University in China.

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Authors and Affiliations

  1. College of Information & Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Haijiang Zhu, Junhui Sheng, Fan Zhang, Jinglin Zhou & Jing Wang

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  1. Haijiang Zhu
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  2. Junhui Sheng
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  3. Fan Zhang
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  4. Jinglin Zhou
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  5. Jing Wang
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Correspondence to Haijiang Zhu.

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Zhu, H., Sheng, J., Zhang, F. et al. Improved maximally stable extremal regions based method for the segmentation of ultrasonic liver images. Multimed Tools Appl 75, 10979–10997 (2016). https://doi.org/10.1007/s11042-015-2822-z

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