Abstract
Rheumatoid arthritis is a chronic inflammatory disease characterized by inflammation of the synovial membrane of the joints. The destruction of the joints results in a narrowing of the joint space, distortion of the joints, and deformity of the fingers. Currently, the mainstream method of diagnosing such symptoms is by direct visual inspection of X-ray images by doctors. However, the diagnosis of the progression of joint destruction requires a lot of time and effort because the number of joints to be observed is large and it is difficult to accurately read minor pathological changes. In addition, the subjective evaluation by the doctors is subject to large intra- and inter-examiner variability and lacks reproducibility. As a means to solve these problems, the goal of this paper has been to develop a diagnosis support system by image processing that is quantitative and labor-saving. In this paper, we propose an automatically joint gap measurement application by using image processing techniques. We are able to extract the joint parts without any excesses or deficiencies by adding processing to remove false positives. Experimental results show that the detection rate of the joint edges is higher than that of the conventional method.
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Goto, T., Goto, K., Funahashi, K. (2022). Automation of Joint Space Distance Measurement for Diagnosis Support System in Rheumatoid Arthritis. In: Chen, YW., Tanaka, S., Howlett, R.J., Jain, L.C. (eds) Innovation in Medicine and Healthcare. Smart Innovation, Systems and Technologies, vol 308. Springer, Singapore. https://doi.org/10.1007/978-981-19-3440-7_19
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DOI: https://doi.org/10.1007/978-981-19-3440-7_19
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