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15 March 2006Detection of joint space narrowing in hand radiographs
Radiographic assessment of joint space narrowing in hand radiographs is important for determining the progression
of rheumatoid arthritis in an early stage. Clinical scoring methods are based on manual measurements that
are time consuming and subjected to intra-reader and inter-reader variance. The goal is to design an automated
method for measuring the joint space width with a higher sensitivity to change1 than manual methods. The large
variability in joint shapes and textures, the possible presence of joint damage, and the interpretation of projection
images make it difficult to detect joint margins accurately. We developed a method that uses a modified
active shape model to scan for margins within a predetermined region of interest. Possible joint space margin
locations are detected using a probability score based on the Mahalanobis distance. To prevent the detection of
false edges, we use a dynamic programming approach. The shape model and the Mahalanobis scoring function
are trained with a set of 50 hand radiographs, in which the margins have been outlined by an expert.
We tested our method on a test set of 50 images. The method was evaluated by calculating the mean absolute
difference with manual readings by a trained person. 90% of the joint margins are detected within 0.12 mm. We
found that our joint margin detection method has a higher precision considering reproducibility than manual
readings. For cases where the joint space has disappeared, the algorithm is unable to estimate the margins. In
these cases it would be necessary to use a different method to quantify joint damage.
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Joost A. Kauffman, Cornelis H. Slump, Hein J. Bernelot Moens, "Detection of joint space narrowing in hand radiographs," Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614446 (15 March 2006); https://doi.org/10.1117/12.653584