3D segmentation and labeling of fractured bone from CT images

The segmentation of fractured bone from computed tomographies (CT images) is an important process in medical visualization and simulation, because it enables such applications to use data of a specific patient. On the other hand, the labeling of fractured bone usually requires the participation of an expert. Moreover, close fragment can be joined after the segmentation because of their proximity and the resolution of the CT image. Classical methods perform well in the segmentation of healthy bone, but they are not able to identify bone fragments separately. In this paper, we propose a method to segment and label bone fragments from CT images. Labeling involves the identification of bone fragments separately. The method is based on 2D region growing and requires minimal user interaction. In addition, the presented method is able to separate wrongly joined fragments during the segmentation process.

[1]  A. Kristan,et al.  Preoperative Planning Program Tool in Treatment of Articular Fractures: Process of Segmentation Procedure , 2010 .

[2]  Jiing-Yih Lai,et al.  VIRTUAL 3D PLANNING OF PELVIC FRACTURE REDUCTION AND IMPLANT PLACEMENT , 2012 .

[3]  Michael Rieger,et al.  Advanced virtual corrective osteotomy , 2005 .

[4]  Maxime Descoteaux,et al.  Bone enhancement filtering: Application to sinus bone segmentation and simulation of pituitary surgery , 2006, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[5]  Jianping Fan,et al.  Seeded region growing: an extensive and comparative study , 2005, Pattern Recognit. Lett..

[6]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[7]  Fabio Baruffaldi,et al.  3D identification of trabecular bone fracture zone using an automatic image registration scheme: A validation study. , 2012, Journal of biomechanics.

[8]  Gábor Székely,et al.  Semi-automatic Segmentation of Fractured Pelvic Bones for Surgical Planning , 2010, ISMBS.

[9]  Hans Knutsson,et al.  Non-Rigid Registration for Automatic Fracture Segmentation , 2006, 2006 International Conference on Image Processing.

[10]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[11]  Charl P. Botha,et al.  Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy , 2012, International Journal of Computer Assisted Radiology and Surgery.

[12]  Ernest M. Stokely,et al.  Medical image segmentation using 3D seeded region growing , 1997, Medical Imaging.

[13]  Sim Heng Ong,et al.  Fast segmentation of bone in CT images using 3D adaptive thresholding , 2010, Comput. Biol. Medicine.

[14]  Kenneth A. Egol,et al.  Handbook Of Fractures , 2002 .