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Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images

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Abstract

An automatic method for cartilage segmentation using knee MRI images is described. Three binary classifiers with integral and partial pixel features are built using the Bayesian theorem to segment the femoral cartilage, tibial cartilage and patellar cartilage separately. First, an iterative procedure based on the feedback of the number of strong edges is designed to obtain an appropriate threshold for the Canny operator and to extract the bone-cartilage interface from MRI images. Second, the different edges are identified based on certain features, which allow for different cartilage to be distinguished synchronously. The cartilage is segmented preliminarily with minimum error Bayesian classifiers that have been previously trained. According to the cartilage edge and its anatomic location, the speed of segmentation is improved. Finally, morphological operations are used to improve the primary segmentation results. The cartilage edge is smooth in the automatic segmentation results and shows good consistency with manual segmentation results. The mean Dice similarity coefficient is 0.761.

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Acknowledgments

This study was sponsored by the National Natural Science Foundation of China (61190122/F0205) and the Natural Science Foundation Project of CQ CSTC (cstc2011jjA10032).

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Correspondence to Mingguo Qiu.

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Jianfei Pang and PengYue Li contributed equally to this work.

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Pang, J., Li, P., Qiu, M. et al. Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images. J Digit Imaging 28, 695–703 (2015). https://doi.org/10.1007/s10278-015-9780-x

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  • DOI: https://doi.org/10.1007/s10278-015-9780-x

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