Objective      To explore the possibilities of automatic segmentation of articular cartilage in the MR images.       Methods      Three binary classifiers were built with edge-distance

intensity and other voxel features using support vector machine to segment the femur cartilage

tibia cartilage and patellar cartilage separately. Firstly

an iterative procedure based on the feedback of the number of main edges was designed to get an appropriate threshold for Canny operator and extract the main edges with it from MRI images. Secondly

the different edges with some feature parameters were identified

which distinguished different cartilages synchronously. To improve the speed of segmentation

the search space was reduced according to the cartilage edge and its anatomic location. Thirdly

the cartilage tissues were segmented preliminarily with different trained classifiers. Finally

morphological operations were used to make those results better.       Results      The cartilage edge was smooth in automatic segmentation results and had a good consistency with manual segmentation results. The mean Dice similarity coefficient of femoral cartilage

tibial cartilage and patellar cartilage was 0.80

0.76 and 0.74

respectively.       Conclusion      The algorithm can segment the articular cartilage automatically from MR images more accurately and quickly.