Literature DB >> 18649479

Proximal femur segmentation in conventional pelvic x ray.

Roland Pilgram1, Claudia Walch, Volker Kuhn, Rainer Schubert, Roland Staudinger.   

Abstract

A solid and accurate proximal femur segmentation technique using the popular active shape model (ASM) is proposed. For generating an optimal shape prior, the minimum description length, based on 200 supervised manual segmented proximal femur shapes, is used. The segmentation is based on a coarse to fine scaling technique including a profile scale space method. The segmentation results are compared using an optimal defined initial pose and a pose based on a registration technique. Using ideal template initialization, 95% of the shapes have been recovered exactly (average point-to-point error approximately 13 pixels, average point-to-boundary error approximately 7 pixels). Using a template-based initialization based on a registration technique, a successful segmentation rate of approximately 89% is achieved, with an average point-to-point error approximately 12 pixels, and an average point-to-boundary error approximately 8 pixels. With an adequate template initialization and an improved ASM, this method seems to provide an accurate tool for segmentation of the proximal femur shapes on conventional hip overview x-ray images.

Mesh:

Year:  2008        PMID: 18649479     DOI: 10.1118/1.2919096

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  2 in total

1.  Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

Authors:  Weiguo Xie; Jochen Franke; Cheng Chen; Paul A Grützner; Steffen Schumann; Lutz-P Nolte; Guoyan Zheng
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-07-31       Impact factor: 2.924

Review 2.  Statistical shape and appearance models in osteoporosis.

Authors:  Isaac Castro-Mateos; Jose M Pozo; Timothy F Cootes; J Mark Wilkinson; Richard Eastell; Alejandro F Frangi
Journal:  Curr Osteoporos Rep       Date:  2014-06       Impact factor: 5.096

  2 in total

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