Abstract—X-ray examinations are widely used in the diagnosis of Rheumatoid Arthritis (RA). However, the condition of many phalanges and joints must be evaluated visually, which causes a lack of objectivity due to subjective evaluation by the physician and an increased workload for the physician in reading the images. In this paper, we propose an image analysis method for hand Computed Radiography (CR) images based on the temporal subtraction method to support the diagnosis of rheumatoid arthritis. The proposal method consists of three steps. First, a Convolutional Neural Network (CNN) model for semantic segmentation, which is efficient in terms of computational complexity and accuracy, is proposed to extract phalangeal regions. Second, a geometric-matching CNN with instance-specific optimization is used to align the phalangeal regions. Finally, the current image and the aligned past image are subtracted to visualize the temporal changes. We applied the proposed method to hand CR images and confirmed its effectiveness.
 
Keywords—Computer Aided Diagnosis (CAD) system, segmentation, image registration, temporal subtraction technique, Rheumatoid Arthritis (RA)


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