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A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

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  • Zhen Ma
  • João Manuel R.S. Tavares
  • Renato Natal Jorge
  • T. Mascarenhas

Abstract

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.

Suggested Citation

  • Zhen Ma & João Manuel R.S. Tavares & Renato Natal Jorge & T. Mascarenhas, 2010. "A review of algorithms for medical image segmentation and their applications to the female pelvic cavity," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(2), pages 235-246.
    • Handle: RePEc:taf:gcmbxx:v:13:y:2010:i:2:p:235-246
    DOI: 10.1080/10255840903131878
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    Cited by:

    1. Alireza Karimi & Seyed Mohammadali Rahmati & Reza Razaghi, 2017. "A combination of experimental measurement, constitutive damage model, and diffusion tensor imaging to characterize the mechanical properties of the human brain," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 20(12), pages 1350-1363, September.
    2. Xiangbin Liu & Liping Song & Shuai Liu & Yudong Zhang, 2021. "A Review of Deep-Learning-Based Medical Image Segmentation Methods," Sustainability, MDPI, vol. 13(3), pages 1-29, January.
    3. Abdul Momin & Naoshi Kondo & Dimas Firmanda Al Riza & Yuichi Ogawa & David Obenland, 2023. "A Methodological Review of Fluorescence Imaging for Quality Assessment of Agricultural Products," Agriculture, MDPI, vol. 13(7), pages 1-14, July.
    4. Jorge Barbosa & Bruno Figueiredo & Nuno Bettencourt & João Tavares, 2011. "Towards automatic quantification of the epicardial fat in non-contrasted CT images," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 14(10), pages 905-914.

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