Int J Comput Assist Radiol Surg - Fully automatic segmentation of the femur from 3D-CT images using primitive shape recognition and statistical shape models.

Tópicos

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Resumo

RPOSE: Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM.METHOD: Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting.RESULTS: The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1?mm.CONCLUSION: The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.

Resumo Limpo

rpose femur segment well establish wide use computerassist orthoped surgeri howev robust segment method statist shape model ssm requir human intervent provid initi posit ssm paper propos overcom problem provid fulli automat femur segment method ct imag base primit shape recognit ssmmethod femur segment ct scan perform use primit shape recognit base robust algorithm hough transform random sampl consensus propos method divid step detect femor head sphere femor shaft cylind ssm ct imag rigid registr primit ssm ct imag initi ssm ct imag fit ssm ct imag edg use affin transform follow nonlinear fittingresult autom method provid good result even high number outlier differ segment error propos automat initi method manual initi method less mmconclus propos method detect primit shape posit initi ssm target imag base primit shape method overcom problem interpati variabl moreov result demonstr method primit shape recognit can use d ssm initi achiev fulli automat segment femur

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