Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Chen, Cheng; Zheng, Guoyan (2013). Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition. In: Wilson, Richard; Hancock, Edwin; Bors, Adrian; Smith, William (eds.) 15th International Conference, CAIP 2013, Proceedings. Lecture Notes in Computer Science: Vol. 8047 (pp. 335-343). Berlin, Heidelberg: Springer 10.1007/978-3-642-40261-6_40

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Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

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