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.
|Publisher||SPIE International Society for Optical Engineering|
|Conference||Medical Imaging 2006: Ultrasonic Imaging and Signal Processing|
|Period||12/02/06 → 16/02/06|
|Other||11-16 Feb 2006|