Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis

  • Authors

    • U Snekhalatha
    • T Rajalakshmi
    • M Gobikrishnan
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10434
  • Canny Edge Detection, Fuzzy C Means Algorithm, Rheumatoid Arthritis, Thermal Imaging Method, X-Ray,

  • Abstract

    Rheumatoid arthritis (RA) is a long lasting autoimmune disorder that affects the multiple joints of human body. The aim and objective of the study was i) to implement the automated segmentation of knee x-ray image and thermal image using fuzzy c means  and canny edge detection algorithm. ii) To compare both the imaging modalities by means of feature extraction and correlate with the biochemical method as standard. Fifteen subjects with RA in knee region and 15 healthy controls were included in this study. The segmentation of thermal images was performed using fuzzy c-means algorithm and x-ray segmentation was implemented using canny edge detection algorithm. The skin surface temperature weremeasured in the thermal image of knee regionin both RA and control subjects. The features wereextracted from the segmented region of the knee x-ray image. The automated segmentation implemented in thermal imaging provided better results compared to x-ray image segmentation process. The thermal imaging feature and x-ray imaging features correlated significantly with the standard parameters.

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  • How to Cite

    Snekhalatha, U., Rajalakshmi, T., & Gobikrishnan, M. (2018). Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis. International Journal of Engineering & Technology, 7(2.8), 326-330. https://doi.org/10.14419/ijet.v7i2.8.10434

    Received date: 2018-03-21

    Accepted date: 2018-03-21

    Published date: 2018-03-19