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International Journal of Computer Assisted Radiology and Surgery

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01-03-2014 | Original Article

Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models

Authors: Lassad Ben Younes, Yoshikazu Nakajima, Toki Saito

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2014

Abstract

Purpose

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.

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Title: Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models
Authors: Lassad Ben Younes
Yoshikazu Nakajima
Toki Saito
Publication date: 01-03-2014
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 2/2014
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-013-0950-3

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Springer Medicine

Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models

Abstract

Purpose

Method

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Method

Results

Conclusion

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