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Region based level sets for image segmentation: a brief comparative review with a fast model FREEST

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Abstract

Region based level sets are one class of popular image segmentation models. Sorting out the inheritance relationship and comparing their performance on same image repositories are of guiding significance. In this paper, we first propose a generalization model to cover external forces of representative region based level sets and give a brief comparative review on them by describing their evolution process and performing a capacity and complexity analysis. We then briefly review regularizations on level sets, known as internal force in the literature. As the models become more and more complicated to perform well on challenging images, it is significant to ensure both segmentation performance and time performance. Thirdly, we propose a fast region based level set (FREEST) model to segment images with intensity inhomogeneities where smoothness of the estimated intensity bias field is ensured by a convolution operation. We then improve FREEST by introducing global intensity variances and rename it as FREESTσ to deal with images with different variances between objects of interest and the background. Experiments on representative 120 images (natural images from BSDS500 and well known synthetic images in the field) with simulated intensity biases show that time performances of the proposed models are close to the simplest but most famous level set model and its time incrementing is only about 1/20 of existing models. Qualitative and quantitative comparison with the representative models on the images in terms of Dice Similarity Coefficient and Jaccard Similarity Coefficient demonstrate advantages of the proposed models. Compared with the second-best model, the evaluation indicators increased by 0.09 and 0.13, respectively. Parameter settings and representative influence are discussed, which indicates robustness of the proposed models. Grand challenges and still open problems such as initialization sensitivity, complex background segmentation, and multi-class object segmentation are finally discussed. Codes will be released if this paper was accepted.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Natural Science Foundation of Liaoning Province of China under grant 2021-MS-085.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Shenyang, Liaoning, 110169, China

    Chaolu Feng & Jinzhu Yang

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, 110169, China

    Chaolu Feng, Shuaizheng Chen, Dazhe Zhao & Jinzhu Yang

  3. Key Laboratory of Medical Image Computing (MIC), Shenyang, Liaoning, 110819, China

    Dazhe Zhao & Jinzhu Yang

Authors
  1. Chaolu Feng
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  2. Shuaizheng Chen
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  3. Dazhe Zhao
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  4. Jinzhu Yang
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Correspondence to Chaolu Feng.

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J. Yang is the Co-corresponding author.

D. Zhao is the Co-first author.

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Feng, C., Chen, S., Zhao, D. et al. Region based level sets for image segmentation: a brief comparative review with a fast model FREEST. Multimed Tools Appl 82, 37065–37095 (2023). https://doi.org/10.1007/s11042-023-15073-x

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  • DOI: https://doi.org/10.1007/s11042-023-15073-x

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