Abstract
The article deals with complex segmentation approach which is focused on detection of pathological changes on the articular cartilage surface. There is a significant problem, in the clinical practice with recognition of individual structures of cartilage surface. Cartilages are normally investigated either by ultrasound or MRI. Both methods give output images in shade level spectrum. This fact is severe problem for detection especially small changes which indicate surface deterioration. This pathological stage often leads to further development of disease which cause to total loss of articular cartilage. The proposed software approach partially solves mentioned problem. The core of segmentation is based on fuzzy C-means algorithm which is very sensitive even in the noisy environment and for tiny structures as well. Furthermore, color transformation is implemented. This key benefit of proposed software allows transformation physiological cartilage to color spectrum, other tissues are suppressed. In the output, we give mathematical model of articular cartilage with detection of pathological discontinuities.
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Acknowledgment
This article has been supported by financial support of TA ČR PRE SEED: TG01010137 GAMA PP1. The work and the contributions were supported by the project SP2015/179 ‘Biomedicínské inženýrské systémy XI’ and This work is partially supported by the Science and Research Fund 2014 of the Moravia-Silesian Region, Czech Republic and this paper has been elaborated in the framework of the project “Support research and development in the Moravian-Silesian Region 2014 DT 1—Research Teams” (RRC/07/2014). Financed from the budget of the Moravian-Silesian Region.
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Kubicek, J., Bryjova, I., Penhaker, M., Kodaj, M., Augustynek, M. (2016). Surface of Articular Cartilage Extraction Using Fuzzy C-means Segmentation. In: Król, D., Madeyski, L., Nguyen, N. (eds) Recent Developments in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-31277-4_18
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DOI: https://doi.org/10.1007/978-3-319-31277-4_18
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