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Image-Based Method for Knee Ligament Injuries Detection

  • Piotr KohutEmail author
  • Rafał Obuchowicz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 934)

Abstract

Anterior cruciate ligament tear is one of the most frequent knee injuries, with reported incidence of 120,000 per year in the US alone. Different forms of everyday activities and contact and noncontact sports associated with pivot shift, dashboard, clip and hyperextension injury may lead to ACL rupture. Various forms of rupture of this important ligament are reported. Among these, the most difficult form to diagnose with imaging methods is reported to be the fan-shaped deformation with fiber discontinuity and no stump formation. Magnetic resonance has proven to be the contemporary gold standard in visualization of the ACL, with specificity and sensitivity reaching 86 and 95%, respectively, in comparison to arthroscopic evaluation. Although high field MRI is very accurate in the evaluation of ACL tears, fatigue and routine of medical professionals is potentially hazardous, and creates the possible risk of overlooking the lesion. Therefore, a semi-automatic diagnostic system is very much awaited by the medical community.

In this paper a new image-based method for the detection of fan-like deformation of acute ACL injury is presented. The developed algorithm is based on digital image processing and analysis. The demonstrated solutions are implemented in the MATLAB environment. The proposed method is investigated and examined on sets of MRI scans with cases of healthy and damaged knee joints. The efficacy of the method is analyzed based on obtained findings, which offered some important conclusions as well.

Keywords

Digital image processing Watershed segmentation Image analysis Knee ligament injuries 

Notes

Acknowledgements

The research reported in this paper has been financed from the state budget for science. The authors wish to acknowledge Sebastian Turek for participation in the reported research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Robotics and MechatronicsAGH University of Science and TechnologyKrakowPoland
  2. 2.Department of RadiologyJagiellonian University Medical CollegeKrakowPoland

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