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Huang, K., Li, Z., Tang, S., Zeng, Y., Ye, W. and Yang, F. (2024) A Level-Set Method with the 3D Multiplicative-Additive Model for CT Volume Data Segmentation. Measurement, 229, Article ID: 114442.
https://doi.org/10.1016/j.measurement.2024.114442

has been cited by the following article:

• TITLE: 3D CT Image Segmentation Based on HMRF-EM and Level Set

  AUTHORS: Danling Liang, Changjiang Liu

  KEYWORDS: GMM, HMRF-EM, Level Set Method, Volume Data

  JOURNAL NAME: Open Access Library Journal, Vol.13 No.1, January 12, 2026

  ABSTRACT: Aiming at the problems of intensity inhomogeneity, boundary blurring and noise interference in the segmentation of three-dimensional volume data (such as medical images and industrial CT data). In this paper, the hidden Markov random field based on Gaussian mixture model is used for the initial segmentation of volume data, and the statistical and spatial information of the image is effectively used to obtain a reasonable initial contour and region division. Then, the level set method is used for fine segmentation to accurately capture the complex boundary shape in the image, which has good adaptability to the change of the target boundary and makes the segmentation boundary more accurately fit the target area. This improvement takes into account the rationality of the initial segmentation and the accuracy of the fine segmentation. The experimental results verify the effectiveness of the proposed method in image segmentation.