Abstract
In many physical and sports activity, knee joint encounters extreme level of stresses leading to traumatic injury. The menisci are located between the tibial plateau and femoral condyles. These menisci act to distribute body weight evenly across the knee joint. Any damage to menisci may lead to uneven weight distribution and cause the development of abnormal excessive forces leading to early damage of the knee joint. Menisci act as ‘shock absorbers’ between femur and tibia, and prevent the lateral movement of the joint when the knee is fully extended. It also helps to provide a lubricating effect on the knee joint, provides some degree of stability, and is essential for the normal biomechanics of the knee joint. The menisci are often injured, particularly during athletics. Magnetic resonance imaging (MRI) is the widely used imaging modality in detection of menisci injuries. There is a chance of missing visibility of menisci tears in MRI during diagnosis. In this work, image processing method based on seeded region growing is developed to detect and visualize menisci tears from MRI. The segmentation accuracy is evaluated using dice similarity coefficient (DSC). The developed method segments the menisci from knee joint MRI with good accuracy and visualizes the tears in different compartments of menisci. The developed method is helpful in treatment and surgery planning of knee joint injured patients.
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Acknowledgements
J. S. S. Medical College and Hospital, J. S. S. University, Mysuru, INDIA for providing MRI data set.
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M. S., M., Raman, R., Holi, M.S., J. S., S.T. (2018). Detection of Menisci Tears in Sports Injured and Pathological Knee Joint Using Image Processing Techniques. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_46
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DOI: https://doi.org/10.1007/978-3-319-71767-8_46
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