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

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.

[1]  Mark Jenkinson,et al.  Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration , 2011, MICCAI.

[2]  Dorin Comaniciu,et al.  Shape Regression Machine , 2007, IPMI.

[3]  Dorin Comaniciu,et al.  Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features , 2008, IEEE Transactions on Medical Imaging.

[4]  Guoyan Zheng,et al.  Automatic extraction of proximal femur contours from calibrated X‐ray images using 3D statistical models: an in vitro study , 2009, The international journal of medical robotics + computer assisted surgery : MRCAS.

[5]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[6]  Ying Chen,et al.  Automatic Extraction of Femur Contours from Hip X-Ray Images , 2005, CVBIA.

[7]  Yi Yang,et al.  3D human pose recovery from image by efficient visual feature selection , 2011, Comput. Vis. Image Underst..

[8]  Junzhou Huang,et al.  Sparse shape composition: A new framework for shape prior modeling , 2011, CVPR 2011.

[9]  David Cristinacce,et al.  Automatic feature localisation with constrained local models , 2008, Pattern Recognit..

[10]  Marleen de Bruijne,et al.  2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models , 2011, Medical Image Anal..

[11]  Antonio Criminisi,et al.  Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences , 2011, MICCAI.

[12]  R Burgkart,et al.  Intraoperative, fluoroscopy‐based planning for complex osteotomies of the proximal femur , 2005, The international journal of medical robotics + computer assisted surgery : MRCAS.

[13]  Hervé Delingette,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 , 2012, Lecture Notes in Computer Science.

[14]  Yanxi Liu,et al.  Computer Vision for Biomedical Image Applications, First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005, Proceedings , 2005, CVBIA.

[15]  Timothy F. Cootes,et al.  Accurate Fully Automatic Femur Segmentation in Pelvic Radiographs Using Regression Voting , 2012, MICCAI.

[16]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Antonio Criminisi,et al.  Regression Forests for Efficient Anatomy Detection and Localization in CT Studies , 2010, MCV.

[18]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, CVPR.