Improved Quantification of Bone Remodelling by Utilizing Fuzzy Based Segmentation

Abstract

We present a novel fuzzy theory based method for the segmentation of images required in histomorphometrical investigations of bone implant integration. The suggested method combines discriminant analysis classification controlled by an introduced uncertainty measure, and fuzzy connectedness segmentation method, so that the former is used for automatic seeding of the later. A thorough evaluation of the proposed segmentation method is performed. Comparison with previously published automatically obtained measurements, as well as with manually obtained ones, is presented. The proposed method improves the segmentation and, consequently, the accuracy of the automatic measurements, while keeping advantages with respect to the manual ones, by being fast, repeatable, and objective

Published in

Lecture Notes in Computer Science
2009, volume: 5575, pages: 750-759
Title: Proceedings SCIA 2009
Publisher: Springer-Verlag

Conference

16th Scandinavian Conference on Image Analysis

SLU Authors

• Lindblad, Joakim

  • Centre for Image Analysis, Swedish University of Agricultural Sciences
    

• Sarve, Hamid

  • Centre for Image Analysis, Swedish University of Agricultural Sciences
    

• Borgefors, Gunilla

  • Centre for Image Analysis, Swedish University of Agricultural Sciences
    

Publication identifier

• DOI: https://doi.org/10.1007/978-3-642-02230-2_77
• ISBN: 978-3-642-02229-6

Permanent link to this page (URI)

https://res.slu.se/id/publ/27208