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Proceedings Paper

Segmentation of cortical bone using fast level sets
Author(s): Manish Chowdhury; Daniel Jörgens; Chunliang Wang; Örjan Smedby; Rodrigo Moreno
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Paper Abstract

Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.

Paper Details

Date Published: 24 February 2017
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013327 (24 February 2017); doi: 10.1117/12.2254240
Show Author Affiliations
Manish Chowdhury, KTH, School of Technology and Health (Sweden)
Daniel Jörgens, KTH, School of Technology and Health (Sweden)
Chunliang Wang, KTH, School of Technology and Health (Sweden)
Örjan Smedby, KTH, School of Technology and Health (Sweden)
Rodrigo Moreno, KTH, School of Technology and Health (Sweden)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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