JOURNAL ARTICLE

Evaluation of optimal interpolation and segmentation of the optic nerves on magnetic resonance images for cross‐sectional area measurement.

  • Published In: International Journal of Imaging Systems & Technology, 2024, v. 34, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chow, Li Sze; Paley, Martyn N. J.; Hickman, Simon J. 3 of 3

Abstract

This study investigated nine combination methods produced from three interpolation (Lanczos, iterative curvature based interpolation, and contrast‐guided) and three segmentation (spatial fuzzy C‐means, modified fuzzy C‐means [mFCM], and level set method) models from optic nerve magnetic resonance images (MRI). The aim was to produce sharp edges of the optic nerves for cross‐sectional area measurement. Lanczos‐mFCM was identified as the best combination in terms of: image quality; Dice similarity coefficient; a similar area compared with measurements from raw images but with higher reproducibility; and the highest signal‐to‐noise and contrast‐to‐noise ratios. It was then applied to ten normal datasets. The mean cross‐sectional areas were 12.57 ± 1.92mm2 from proton density images, 12.98 ± 2.18mm2 from T2‐weighted (T2W) images including the optic nerve sheath, and 1.68 ± 0.69mm2 from the T2W images of the optic nerve only. The Lanczos‐mFCM method is recommended for future quantitative studies on optic nerve MRI. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Imaging Systems & Technology. 2024/03, Vol. 34, Issue 2, p1
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2024
  • ISSN:0899-9457
  • DOI:10.1002/ima.23030
  • Accession Number:176274784
  • Copyright Statement:Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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