Detection of Rheumatoid Arthritis using Image Processing Techniques
DOI:
https://doi.org/10.61841/dacn4t78Keywords:
Canny edge detection, Median filtering, Phalangeal bones, Rheumatoid arthritis, X-rayAbstract
Rheumatoid arthritis (RA) is a kind of inflammatory disease. Main symptoms of RA are inflammation, swelling, and joint pain, especially in the early hours of the day. Inflammation starts at smaller joints of the body; in later stages, inflammation spreads to the heart and other organs of the body. Therefore, detection of RA in the early stages is very essential. Different modalities are being used for the purpose of RA diagnosis, notably radiography, ultrasound, and magnetic resonance imaging (MRI), even though X-rays are the best and most effective tool in the assessment of joint damage and position changes. The gap between phalangeal bones in the hand finger is a vital parameter in the detection of RA using X-ray images, especially the metacarpal joint and proximal joint involved in the early stage of the RA. This work deals with the development of the image processing technique, which is helpful for RA detection. The image processing steps involve median filtering, background extraction, image subtraction, canny edge detection for segmentation, and finally feature extraction. The extracted feature reveals the significant difference between normal and abnormal (RA) images. The dataset includes both normal and RA-affected images.
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