Abstract
This article addresses a possible approach for a higher quality diagnosis and detection of the pathological defects of articular cartilage. The defects of articular cartilage are one of the most common pathologies of articular cartilage that a physician encounters. In clinical practice, doctors can only estimate visually whether or not there is a pathological defect with the use of magnetic resonance images. Our proposed methodology is able to accurately and precisely localize ruptures of cartilaginous tissue and thus greatly contribute to improving a final diagnosis. When analysing MRI data, we work only with grey-levels, which is rather complicated for producing a quality diagnosis. Our proposed algorithm, based on fuzzy logic, brings together various shades of grey. Each set is assigned a colour that corresponds to the density of the tissue. With this procedure, it is possible to create a contrast map of individual tissue structures and very clearly identify where cartilaginous tissues have been interrupted. The suggested methodology has been tested using real data from magnetic resonance images of 60 patients from Podlesí Hospital in Třinec and currently this method is being put into clinical practice.
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Kubicek, J., Penhaker, M., Bryjova, I., Kodaj, M. (2014). Articular Cartilage Defect Detection Based on Image Segmentation with Colour Mapping. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_22
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DOI: https://doi.org/10.1007/978-3-319-11289-3_22
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