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An Adrenal Segmentation Model Based on Shape Associating Level Set in Sequence of CT Images

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A Correction to this article was published on 19 November 2019

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Abstract

The segmentation challenge of adrenal and surrounding tissues lie in the similar CT values and adhesion in a medical image. An adrenal segmentation model (SALS) based on shape associating level set is proposed to segment the adrenal accurately. The objective function of adrenal boundary is expressed by a level set model. The prior shape curve of the adrenal to be segmented is calculated by the gradual change relationship between the adrenal glands in adjacent CT images, and is converted into shape constraint term of the level set models. A 3D Laplace method is used to improve adrenal grey scale information. The improved result of CT image is converted into grey scale constraint term of the level set models. Under the two constraints, the objective function of adrenal boundary converges to the best boundary. The level set methods in the literature obtain a prior shape by training a large number of sample images, and use the shape modes to segment CT images. The SALS model does not depend on the sample images. The adrenal boundaries in sequence of CT images can be directly segmented by SALS model, and the segmented boundaries are more accurate than the traditional level set methods. The SALS model has stronger adaptability to adherent adrenal boundary.

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Change history

  • 19 November 2019

    The Publisher regrets an error on the printed front cover of the October 2019 issue. The issue numbers were incorrectly listed as Volume 91, Nos. 10-12, October 2019. The correct number should be: "Volume 91, No. 10, October 2019"

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Authors and Affiliations

  1. China University of Mining and Technology, Beijing campus, Ding 11, Xueyuan Road, Haidian District, Beijing, China

    Guoying Zhang & Zhiwei Li

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  1. Guoying Zhang
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  2. Zhiwei Li
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Correspondence to Zhiwei Li.

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Zhang, G., Li, Z. An Adrenal Segmentation Model Based on Shape Associating Level Set in Sequence of CT Images. J Sign Process Syst 91, 1169–1177 (2019). https://doi.org/10.1007/s11265-018-1433-0

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  • DOI: https://doi.org/10.1007/s11265-018-1433-0

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