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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Ultrasound image segmentation: a survey.

J Alison Noble1, Djamal Boukerroui

  • 1Department of Engineering Science, University of Oxford, UK. noble@robots.ox.ac.uk

IEEE Transactions on Medical Imaging
|August 10, 2006
PubMed
Summary

This review explores medical B-mode ultrasound segmentation methods, classifying techniques by prior information use. It highlights clinically useful approaches and identifies ten key papers for future research in ultrasound imaging.

Area of Science:

  • Medical Imaging
  • Image Analysis
  • Computational Biology

Background:

  • Medical B-mode ultrasound imaging is crucial for diagnosis.
  • Accurate segmentation of ultrasound images is challenging due to image artifacts and variability.
  • Automated segmentation methods are needed to improve efficiency and consistency in clinical practice.

Purpose of the Study:

  • To provide a comprehensive review of ultrasound segmentation methods.
  • To classify existing techniques based on their use of prior information.
  • To identify seminal papers with significant clinical utility or potential in ultrasound segmentation.

Main Methods:

  • Systematic review of published literature on ultrasound segmentation.
  • Classification of methods based on the integration of prior knowledge (e.g., anatomical atlases, statistical models).

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  • Analysis of clinical applications and validation levels across different medical domains.

    • Main Results:

    • Categorization of ultrasound segmentation techniques, distinguishing between methods that leverage prior information and those that do not.
    • Identification of research trends and validation efforts across various clinical applications.
    • Selection of ten high-impact papers demonstrating novel ideas and clinical relevance.

    Conclusions:

    • The review offers a structured overview of the ultrasound segmentation landscape.
    • Prior information plays a critical role in the performance and applicability of segmentation algorithms.
    • The identified key papers represent significant contributions and potential future directions for ultrasound image analysis.