Anatomical structure characterization of fetal ultrasound images using texture-based segmentation technique via an interactive MATLAB application.

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    • Source:
      Publisher: Wiley Subscription Services, Inc Country of Publication: United States NLM ID: 0401663 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0096 (Electronic) Linking ISSN: 00912751 NLM ISO Abbreviation: J Clin Ultrasound Subsets: MEDLINE
    • Publication Information:
      Publication: <2005-> : Hoboken, N.J. : Wiley Subscription Services, Inc.
      Original Publication: Denver, Colo. : Norman House, c1973-
    • Subject Terms:
    • Abstract:
      Objective: To describe the texture characteristics in several anatomical structures within fetal ultrasound images by applying an image segmentation technique through an application developed in MATLAB mathematical processing software.
      Methods: Prospective descriptive observational study with an analytical component. 2D fetal ultrasound images were acquired in patients admitted to the Maternal Fetal Medicine Unit of the Hospital de San José, Bogotá-Colombia. These images were loaded into the developed application to carry out the segmentation and characterization stages by means of 23 numerical texture descriptors. The data were analyzed with central tendency measures and through an embedding process and Euclidean distance.
      Results: Forty ultrasound images were included, characterizing 54 structures of the fetal placenta, skull, thorax, and abdomen. By embedding the descriptors, the differentiation of biologically known structures as distinct was achieved, as well as the non-differentiation of similar structures, evidenced using 2D and 3D graphs and numerical data with statistical significance.
      Conclusion: The texture characterization of the labeled structures in fetal ultrasound images through the numerical descriptors allows the accurate discrimination of these structures.
      (© 2023 Wiley Periodicals LLC.)
    • References:
      Burstein L. Primary MATLAB® for Life Sciences. Guide for Beginners. 2013.
      Demirkaya O, Asyali MH, Sahoo PK. Image Processing with MATLAB: Applications in Medicine and Biology. CRC Press; 2008.
      Rafael C, Gonzalez REW, Eddins SL. Digital Image Processing Using. Prentice-Hall, Inc.; 2003.
      Qidwai U, Chen C-h. Digital Image Processing: an Algorithmic Approach with MATLAB. CRC press; 2009.
      Dass R, Devi S. Image segmentation techniques 1. 2012.
      Al-Amri SS, Kalyankar NV. Image segmentation by using threshold techniques. arXiv Preprint arXiv:10054020. 2010.
      Kaur D, Kaur Y. Various image segmentation techniques: a review. Int J Comput Sci Mob Comput. 2014;3(5):809-814.
      Norouzi A, Rahim MSM, Altameem A, et al. Medical image segmentation methods, algorithms, and applications. IETE Tech Rev. 2014;31(3):199-213.
      Reyes-Aldasoro CC. Biomedical Image Analysis Recipes in MATLAB: for Life Scientists and Engineers. John Wiley & Sons; 2015.
      Rueda S, Fathima S, Knight CL, et al. Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imaging. 2014;33(4):797-813.
      Van der Maaten L, Hinton G. Visualizing Data using t-SNE. J Mach Learn Res. 2008;9(86):2579-2605.
      Lee L, Liew S. Breast ultrasound automated ROI segmentation with region growing. 4th International Conference on Software Engineering and Computer Systems (ICSECS). 2015 177-182.
      ISUOG. ISUOG practice guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet Gynecol. 2019;53(6):715-723.
      Buitrago Leal M, Beltran Avendaño M, Molina GS. Guias para la realización de ultrasonido obstétrico II y III trimestre. Federación Colombiana de Asociaciones Ed perinatología - FECOPEN. Guias y suplementos. 2014;1:13-22.
      Anjna EA, Er RK. Review of image segmentation technique. Int J Adv Res Comput Sci. 2017;8(4):36-39.
      Sobhaninia Z, Rafiei S, Emami A, et al. Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. Conf Proc IEEE Eng Med Biol Soc. 2019;2019:6545-6548.
      Sree SJ, Vasanthanayaki C. Ultrasound fetal image segmentation techniques: a review. Curr Med Imaging Rev. 2019;15(1):52-60.
      Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine learning in fetal cardiology: what to expect. Fetal Diagn Ther. 2020;47(5):363-372.
      Xie H, Wang N, He M, et al. Using deep learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound Obstet Gynecol. 2020;56:579-587.
      Oguz I, Yushkevich N, Pouch A, et al. Minimally interactive placenta segmentation from three-dimensional ultrasound images. J Med Imaging (Bellingham). 2020;7(1):014004.
    • Contributed Indexing:
      Keywords: MATLAB; diagnostic imaging; image segmentation; prenatal diagnosis; prenatal ultrasonography
    • Publication Date:
      Date Created: 20231123 Date Completed: 20240214 Latest Revision: 20240214
    • Publication Date:
      20240214
    • Accession Number:
      10.1002/jcu.23604
    • Accession Number:
      37994115