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Finger Joint Detection Method in Hand X-ray Radiograph Images Using Statistical Shape Model and Support Vector Machine

The number of rheumatoid arthritis (RA) patients is about 700,000 in Japan. The modified total sharp score (mTSS) calculated from hand X-ray image is a standard diagnosis method of RA progression but can be time-consuming for physicians. Computer-aided diagnostic (CAD) system is expected to improve the diagnostic quality of RA patients. We have previously proposed a CAD system, which can detect finger joint positions using support vector machine and estimate mTSS using support vector regression. This study improves the finger joint detection accuracy by incorporating with statistical shape model, which statistically models individual variety of spatial relationship among finger joints. Experimental results on radiographic images of the hands of 90 RA patients showed that the finger joints were detected with an accuracy of 94.5%, which is higher than 90.6% of the accuracy with the previous method.